Allensmile / Awesome-Transformer-Attention

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

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Ultimate-Awesome-Transformer-Attention Awesome

This repo contains a comprehensive paper list of Vision Transformer & Attention, including papers, codes, and related websites.
This list is maintained by Min-Hung Chen. (Actively keep updating)

If you find some ignored papers, feel free to open issues or create pull requests.
Contributions in any form to make this list more comprehensive are welcome.

If you find this repository useful, please consider citing and STARing this list.
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Overview


Image Classification / Backbone

Replace Conv w/ Attention

Pure Attention

Conv-stem + Attention

  • GSA: "Global Self-Attention Networks for Image Recognition", arXiv, 2020 (Google). [Paper][PyTorch (lucidrains)]
  • HaloNet: "Scaling Local Self-Attention For Parameter Efficient Visual Backbones", CVPR, 2021 (Google). [Paper][PyTorch (lucidrains)]
  • CoTNet: "Contextual Transformer Networks for Visual Recognition", CVPRW, 2021 (JD). [Paper][PyTorch]
  • TransCNN: "Transformer in Convolutional Neural Networks", arXiv, 2021 (ETHZ). [Paper]

Conv + Attention

[Back to Overview]

Vision Transformer

General Vision Transformer

  • ViT: "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale", ICLR, 2021 (Google). [Paper][Tensorflow][PyTorch (lucidrains)]
  • Perceiver: "Perceiver: General Perception with Iterative Attention", ICML, 2021 (DeepMind). [Paper][PyTorch (lucidrains)]
  • PiT: "Rethinking Spatial Dimensions of Vision Transformers", ICCV, 2021 (NAVER). [Paper][PyTorch]
  • VT: "Visual Transformers: Where Do Transformers Really Belong in Vision Models?", ICCV, 2021 (Facebook). [Paper][PyTorch (tahmid0007)]
  • PVT: "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions", ICCV, 2021 (Nanjing University). [Paper][PyTorch]
  • iRPE: "Rethinking and Improving Relative Position Encoding for Vision Transformer", ICCV, 2021 (Microsoft). [Paper][PyTorch]
  • CaiT: "Going deeper with Image Transformers", ICCV, 2021 (Facebook). [Paper][PyTorch]
  • Swin-Transformer: "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows", ICCV, 2021 (Microsoft). [Paper][PyTorch (berniwal)][Code]
  • T2T-ViT: "Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet", ICCV, 2021 (Yitu). [Paper][PyTorch]
  • FFNBN: "Leveraging Batch Normalization for Vision Transformers", ICCVW, 2021 (Microsoft). [Paper]
  • DPT: "DPT: Deformable Patch-based Transformer for Visual Recognition", ACMMM, 2021 (CAS). [Paper][PyTorch]
  • Focal: "Focal Attention for Long-Range Interactions in Vision Transformers", NeurIPS, 2021 (Microsoft). [Paper][PyTorch]
  • XCiT: "XCiT: Cross-Covariance Image Transformers", NeurIPS, 2021 (Facebook). [Paper]
  • Twins: "Twins: Revisiting Spatial Attention Design in Vision Transformers", NeurIPS, 2021 (Meituan). [Paper][PyTorch)]
  • ARM: "Blending Anti-Aliasing into Vision Transformer", NeurIPS, 2021 (Amazon). [Paper][GitHub (in construction)]
  • DVT: "Not All Images are Worth 16x16 Words: Dynamic Vision Transformers with Adaptive Sequence Length", NeurIPS, 2021 (Tsinghua). [Paper][PyTorch]
  • Aug-S: "Augmented Shortcuts for Vision Transformers", NeurIPS, 2021 (Huawei). [Paper]
  • TNT: "Transformer in Transformer", NeurIPS, 2021 (Huawei). [Paper][PyTorch][PyTorch (lucidrains)]
  • ViTAE: "ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias", NeurIPS, 2021 (The University of Sydney). [Paper][PyTorch]
  • DeepViT: "DeepViT: Towards Deeper Vision Transformer", arXiv, 2021 (NUS + ByteDance). [Paper][Code]
  • So-ViT: "So-ViT: Mind Visual Tokens for Vision Transformer", arXiv, 2021 (Dalian University of Technology). [Paper][PyTorch]
  • LV-ViT: "All Tokens Matter: Token Labeling for Training Better Vision Transformers", NeurIPS, 2021 (ByteDance). [Paper][PyTorch]
  • NesT: "Aggregating Nested Transformers", arXiv, 2021 (Google). [Paper][Tensorflow]
  • LIT: "Less is More: Pay Less Attention in Vision Transformers", arXiv, 2021 (Monash University). [Paper][PyTorch]
  • MSG-Transformer: "MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens", arXiv, 2021 (Huazhong University of Science & Technology). [Paper][PyTorch]
  • KVT: "KVT: k-NN Attention for Boosting Vision Transformers", arXiv, 2021 (Alibaba). [Paper]
  • Refined-ViT: "Refiner: Refining Self-attention for Vision Transformers", arXiv, 2021 (NUS, Singapore). [Paper][PyTorch]
  • Shuffle-Transformer: "Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer", arXiv, 2021 (Tencent). [Paper]
  • CAT: "CAT: Cross Attention in Vision Transformer", arXiv, 2021 (KuaiShou). [Paper][PyTorch]
  • V-MoE: "Scaling Vision with Sparse Mixture of Experts", arXiv, 2021 (Google). [Paper]
  • P2T: "P2T: Pyramid Pooling Transformer for Scene Understanding", arXiv, 2021 (Nankai University). [Paper]
  • PvTv2: "PVTv2: Improved Baselines with Pyramid Vision Transformer", arXiv, 2021 (Nanjing University). [Paper][PyTorch]
  • LG-Transformer: "Local-to-Global Self-Attention in Vision Transformers", arXiv, 2021 (IIAI, UAE). [Paper]
  • ViP: "Visual Parser: Representing Part-whole Hierarchies with Transformers", arXiv, 2021 (Oxford). [Paper]
  • Scaled-ReLU: "Scaled ReLU Matters for Training Vision Transformers", AAAI, 2022 (Alibaba). [Paper]
  • DTN: "Dynamic Token Normalization Improves Vision Transformer", ICLR, 2022 (Tencent). [Paper][PyTorch (in construction)]
  • RegionViT: "RegionViT: Regional-to-Local Attention for Vision Transformers", ICLR, 2022 (MIT-IBM Watson). [Paper][PyTorch]
  • CrossFormer: "CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention", ICLR, 2022 (Zhejiang University). [Paper][PyTorch]
  • ?: "Scaling the Depth of Vision Transformers via the Fourier Domain Analysis", ICLR, 2022 (UT Austin). [Paper]
  • ViT-G: "Scaling Vision Transformers", CVPR, 2022 (Google). [Paper]
  • CSWin: "CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows", CVPR, 2022 (Microsoft). [Paper][PyTorch]
  • MPViT: "MPViT: Multi-Path Vision Transformer for Dense Prediction", CVPR, 2022 (KAIST). [Paper][PyTorch]
  • Diverse-ViT: "The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy", CVPR, 2022 (UT Austin). [Paper][PyTorch]
  • DW-ViT: "Beyond Fixation: Dynamic Window Visual Transformer", CVPR, 2022 (Dark Matter AI, China). [Paper][PyTorch (in construction)]
  • MixFormer: "MixFormer: Mixing Features across Windows and Dimensions", CVPR, 2022 (Baidu). [Paper][Paddle]
  • BViT: "BViT: Broad Attention based Vision Transformer", arXiv, 2022 (CAS). [Paper]
  • PyramidTNT: "PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture", arXiv, 2022 (Huawei). [Paper][PyTorch]
  • DAT: "Vision Transformer with Deformable Attention", arXiv, 2022 (Tsinghua). [Paper][PyTorch]
  • O-ViT: "O-ViT: Orthogonal Vision Transformer", arXiv, 2022 (East China Normal University). [Paper]
  • MOA-Transformer: "Aggregating Global Features into Local Vision Transformer", arXiv, 2022 (University of Kansas). [Paper][PyTorch]
  • BOAT: "BOAT: Bilateral Local Attention Vision Transformer", arXiv, 2022 (Baidu + HKU). [Paper]
  • ViTAEv2: "ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond", arXiv, 2022 (The University of Sydney). [Paper]
  • VAN: "Visual Attention Network", arXiv, 2022 (Tsinghua). [Paper][PyTorch]
  • HiP: "Hierarchical Perceiver", arXiv, 2022 (DeepMind). [Paper]
  • PatchMerger: "Learning to Merge Tokens in Vision Transformers", arXiv, 2022 (Google). [Paper]
  • DGT: "Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention", arXiv, 2022 (Baidu). [Paper]
  • ScalableViT: "ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer", arXiv, 2022 (ByteDance). [Paper]
  • MaxViT: "MaxViT: Multi-Axis Vision Transformer", arXiv, 2022 (Google). [Paper]
  • DaViT: "DaViT: Dual Attention Vision Transformers", arXiv, 2022 (HKU). [Paper][PyTorch]
  • NAT: "Neighborhood Attention Transformer", arXiv, 2022 (Oregon). [Paper][Code (in construction)]
  • VSA: "VSA: Learning Varied-Size Window Attention in Vision Transformers", arXiv, 2022 (The University of Sydney). [Paper][Code (in construction)]
  • ASF-former: "Adaptive Split-Fusion Transformer", arXiv, 2022 (Fudan). [Paper][PyTorch (in construction)]

Efficient Vision Transformer

  • DeiT: "Training data-efficient image transformers & distillation through attention", ICML, 2021 (Facebook). [Paper][PyTorch]
  • ConViT: "ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases", ICML, 2021 (Facebook). [Paper][Code]
  • ?: "Improving the Efficiency of Transformers for Resource-Constrained Devices", DSD, 2021 (NavInfo Europe, Netherlands). [Paper]
  • PS-ViT: "Vision Transformer with Progressive Sampling", ICCV, 2021 (CPII). [Paper]
  • HVT: "Scalable Visual Transformers with Hierarchical Pooling", ICCV, 2021 (Monash University). [Paper][PyTorch]
  • CrossViT: "CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification", ICCV, 2021 (MIT-IBM). [Paper][PyTorch]
  • ViL: "Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding", ICCV, 2021 (Microsoft). [Paper][PyTorch]
  • Visformer: "Visformer: The Vision-friendly Transformer", ICCV, 2021 (Beihang University). [Paper][PyTorch]
  • MultiExitViT: "Multi-Exit Vision Transformer for Dynamic Inference", BMVC, 2021 (Aarhus University, Denmark). [Paper][Tensorflow]
  • SViTE: "Chasing Sparsity in Vision Transformers: An End-to-End Exploration", NeurIPS, 2021 (UT Austin). [Paper][PyTorch]
  • DGE: "Dynamic Grained Encoder for Vision Transformers", NeurIPS, 2021 (Megvii). [Paper][PyTorch]
  • GG-Transformer: "Glance-and-Gaze Vision Transformer", NeurIPS, 2021 (JHU). [Paper][Code (in construction)]
  • DynamicViT: "DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification", NeurIPS, 2021 (Tsinghua). [Paper][PyTorch][Website]
  • ResT: "ResT: An Efficient Transformer for Visual Recognition", NeurIPS, 2021 (Nanjing University). [Paper][PyTorch]
  • Adder-Transformer: "Adder Attention for Vision Transformer", NeurIPS, 2021 (Huawei). [Paper]
  • SOFT: "SOFT: Softmax-free Transformer with Linear Complexity", NeurIPS, 2021 (Fudan). [Paper][PyTorch][Website]
  • IA-RED2: "IA-RED2: Interpretability-Aware Redundancy Reduction for Vision Transformers", NeurIPS, 2021 (MIT-IBM). [Paper][Website]
  • LocalViT: "LocalViT: Bringing Locality to Vision Transformers", arXiv, 2021 (ETHZ). [Paper][PyTorch]
  • CCT: "Escaping the Big Data Paradigm with Compact Transformers", arXiv, 2021 (University of Oregon). [Paper][PyTorch]
  • DiversePatch: "Vision Transformers with Patch Diversification", arXiv, 2021 (UT Austin + Facebook). [Paper][PyTorch]
  • SL-ViT: "Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead", arXiv, 2021 (Aarhus University). [Paper]
  • PS-ViT: "Patch Slimming for Efficient Vision Transformers", arXiv, 2021 (Huawei). [Paper]
  • ?: "Multi-Exit Vision Transformer for Dynamic Inference", arXiv, 2021 (Aarhus University, Denmark). [Paper]
  • DeiT-Manifold: "Efficient Vision Transformers via Fine-Grained Manifold Distillation", arXiv, 2021 (Huawei). [Paper]
  • ViX: "Vision Xformers: Efficient Attention for Image Classification", arXiv, 2021 (Indian Institute of Technology Bombay). [Paper]
  • Transformer-LS: "Long-Short Transformer: Efficient Transformers for Language and Vision", NeurIPS, 2021 (NVIDIA). [Paper][PyTorch]
  • WideNet: "Go Wider Instead of Deeper", arXiv, 2021 (NUS). [Paper]
  • Armour: "Armour: Generalizable Compact Self-Attention for Vision Transformers", arXiv, 2021 (Arm). [Paper]
  • Mobile-Former: "Mobile-Former: Bridging MobileNet and Transformer", arXiv, 2021 (Microsoft). [Paper]
  • IPE: "Exploring and Improving Mobile Level Vision Transformers", arXiv, 2021 (CUHK). [Paper]
  • DS-Net++: "DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers", arXiv, 2021 (Monash University). [Paper][PyTorch]
  • UFO-ViT: "UFO-ViT: High Performance Linear Vision Transformer without Softmax", arXiv, 2021 (Kakao). [Paper]
  • Token-Pooling: "Token Pooling in Visual Transformers", arXiv, 2021 (Apple). [Paper]
  • SReT: "Sliced Recursive Transformer", arXiv, 2021 (CMU + MBZUAI). [Paper][PyTorch]
  • Evo-ViT: "Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer", AAAI, 2022 (Tencent). [Paper][PyTorch]
  • PS-Attention: "Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention", AAAI, 2022 (Baidu). [Paper][Paddle]
  • ShiftViT: "When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism", AAAI, 2022 (Microsoft). [Paper][PyTorch]
  • EViT: "Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations", ICLR, 2022 (Tencent). [Paper][PyTorch]
  • QuadTree: "QuadTree Attention for Vision Transformers", ICLR, 2022 (Simon Fraser + Alibaba). [Paper][PyTorch]
  • Anti-Oversmoothing: "Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice", ICLR, 2022 (UT Austin). [Paper][PyTorch]
  • QnA: "Learned Queries for Efficient Local Attention", CVPR, 2022 (Tel-Aviv). [Paper][Jax]
  • LVT: "Lite Vision Transformer with Enhanced Self-Attention", CVPR, 2022 (Adobe). [Paper][PyTorch]
  • TerViT: "TerViT: An Efficient Ternary Vision Transformer", arXiv, 2022 (Beihang University). [Paper]
  • MT-ViT: "Multi-Tailed Vision Transformer for Efficient Inference", arXiv, 2022 (Wuhan University). [Paper]
  • ViT-P: "ViT-P: Rethinking Data-efficient Vision Transformers from Locality", arXiv, 2022 (Chongqing University of Technology). [Paper]
  • CF-ViT: "Coarse-to-Fine Vision Transformer", arXiv, 2022 (Xiamen University + Tencent). [Paper][PyTorch]
  • EIT: "EIT: Efficiently Lead Inductive Biases to ViT", arXiv, 2022 (Academy of Military Sciences, China). [Paper]
  • SepViT: "SepViT: Separable Vision Transformer", arXiv, 2022 (University of Electronic Science and Technology of China). [Paper]
  • ATS: "Adaptive Inverse Transform Sampling For Efficient Vision Transformers", arXiv, 2022 (Microsoft). [Paper]
  • ResT-V2: "ResT V2: Simpler, Faster and Stronger", arXiv, 2022 (Nanjing University). [Paper][PyTorch]
  • EdgeViT: "EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers", arXiv, 2022 (Samsung). [Paper]

Conv + Transformer

  • LeViT: "LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference", ICCV, 2021 (Facebook). [Paper][PyTorch]
  • CeiT: "Incorporating Convolution Designs into Visual Transformers", ICCV, 2021 (SenseTime). [Paper][PyTorch (rishikksh20)]
  • Conformer: "Conformer: Local Features Coupling Global Representations for Visual Recognition", ICCV, 2021 (CAS). [Paper][PyTorch]
  • CoaT: "Co-Scale Conv-Attentional Image Transformers", ICCV, 2021 (UCSD). [Paper][PyTorch]
  • CvT: "CvT: Introducing Convolutions to Vision Transformers", ICCV, 2021 (Microsoft). [Paper][Code]
  • ViTc: "Early Convolutions Help Transformers See Better", NeurIPS, 2021 (Facebook). [Paper]
  • ConTNet: "ConTNet: Why not use convolution and transformer at the same time?", arXiv, 2021 (ByteDance). [Paper][PyTorch]
  • CMT: "CMT: Convolutional Neural Networks Meet Vision Transformers", arXiv, 2021 (Huawei). [Paper]
  • SPACH: "A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP", arXiv, 2021 (Microsoft). [Paper]
  • MobileViT: "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer", ICLR, 2022 (Apple). [Paper]
  • CXV: "Convolutional Xformers for Vision", arXiv, 2022 (IIT Bombay). [Paper][PyTorch]
  • ConvMixer: "Patches Are All You Need?", arXiv, 2022 (CMU). [Paper][PyTorch]
  • UniFormer: "UniFormer: Unifying Convolution and Self-attention for Visual Recognition", arXiv, 2022 (SenseTime). [Paper][PyTorch]
  • EdgeFormer: "EdgeFormer: Improving Light-weight ConvNets by Learning from Vision Transformers", arXiv, 2022 (?). [Paper]

Training + Transformer

  • iGPT: "Generative Pretraining From Pixels", ICML, 2020 (OpenAI). [Paper][Tensorflow]
  • MoCo-V3: "An Empirical Study of Training Self-Supervised Vision Transformers", ICCV, 2021 (Facebook). [Paper]
  • DINO: "Emerging Properties in Self-Supervised Vision Transformers", ICCV, 2021 (Facebook). [Paper][PyTorch]
  • drloc: "Efficient Training of Visual Transformers with Small Datasets", NeurIPS, 2021 (University of Trento). [Paper][PyTorch]
  • CARE: "Revitalizing CNN Attentions via Transformers in Self-Supervised Visual Representation Learning", NeurIPS, 2021 (Tencent). [Paper][PyTorch]
  • MST: "MST: Masked Self-Supervised Transformer for Visual Representation", NeurIPS, 2021 (SenseTime). [Paper]
  • SiT: "SiT: Self-supervised Vision Transformer", arXiv, 2021 (University of Surrey). [Paper][PyTorch]
  • MoBY: "Self-Supervised Learning with Swin Transformers", arXiv, 2021 (Microsoft). [Paper][Pytorch]
  • ?: "Investigating Transfer Learning Capabilities of Vision Transformers and CNNs by Fine-Tuning a Single Trainable Block", arXiv, 2021 (Pune Institute of Computer Technology, India). [Paper]
  • MaskFeat: "Masked Feature Prediction for Self-Supervised Visual Pre-Training", arXiv, 2021 (Facebook). [Paper]
  • Annotations-1.3B: "Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations", WACV, 2022 (Pinterest). [Paper]
  • BEiT: "BEiT: BERT Pre-Training of Image Transformers", ICLR, 2022 (Microsoft). [Paper][PyTorch]
  • EsViT: "Efficient Self-supervised Vision Transformers for Representation Learning", ICLR, 2022 (Microsoft). [Paper]
  • iBOT: "Image BERT Pre-training with Online Tokenizer", ICLR, 2022 (ByteDance). [Paper][PyTorch]
  • AutoProg: "Automated Progressive Learning for Efficient Training of Vision Transformers", CVPR, 2022 (Monash University, Australia). [Paper][Code (in construction)]
  • MAE: "Masked Autoencoders Are Scalable Vision Learners", CVPR, 2022 (Facebook). [Paper][PyTorch][PyTorch (pengzhiliang)]
  • PeCo: "PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers", arXiv, 2022 (Microsoft). [Paper]
  • IDMM: "Training Vision Transformers with Only 2040 Images", arXiv, 2022 (Nanjing University). [Paper]
  • RePre: "RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training", arXiv, 2022 (Beijing University of Posts and Telecommunications). [Paper]
  • Beyond-Masking: "Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers", arXiv, 2022 (CAS). [Paper][Code (in construction)]
  • Kronecker-Adaptation: "Parameter-efficient Fine-tuning for Vision Transformers", arXiv, 2022 (Microsoft). [Paper]
  • HAT: "Improving Vision Transformers by Revisiting High-frequency Components", arXiv, 2022 (Tsinghua). [Paper]
  • DILEMMA: "DILEMMA: Self-Supervised Shape and Texture Learning with Transformers", arXiv, 2022 (University of Bern, Switzerland). [Paper]
  • DeiT-III: "DeiT III: Revenge of the ViT", arXiv, 2022 (Meta). [Paper]
  • ?: "Better plain ViT baselines for ImageNet-1k", arXiv, 2022 (Google). [Paper][Tensorflow]
  • ConvMAE: "ConvMAE: Masked Convolution Meets Masked Autoencoders", arXiv, 2022 (Shanghai AI Laboratory). [Paper][PyTorch (in construction)]

Robustness + Transformer

  • ViT-Robustness: "Understanding Robustness of Transformers for Image Classification", ICCV, 2021 (Google). [Paper]
  • SAGA: "On the Robustness of Vision Transformers to Adversarial Examples", ICCV, 2021 (University of Connecticut). [Paper]
  • ?: "Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs", BMVC, 2021 (KAIST). [Paper][PyTorch]
  • ViTs-vs-CNNs: "Are Transformers More Robust Than CNNs?", NeurIPS, 2021 (JHU). [Paper][PyTorch]
  • RVT: "Rethinking the Design Principles of Robust Vision Transformer", arXiv, 2021 (Alibaba). [Paper][PyTorch]
  • T-CNN: "Transformed CNNs: recasting pre-trained convolutional layers with self-attention", arXiv, 2021 (Facebook). [Paper]
  • Transformer-Attack: "On the Adversarial Robustness of Visual Transformers", arXiv, 2021 (Xi'an Jiaotong). [Paper]
  • ?: "Reveal of Vision Transformers Robustness against Adversarial Attacks", arXiv, 2021 (University of Rennes). [Paper]
  • ?: "On Improving Adversarial Transferability of Vision Transformers", arXiv, 2021 (ANU). [Paper][PyTorch]
  • ?: "Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers", arXiv, 2021 (University of Pittsburgh). [Paper]
  • Token-Attack: "Adversarial Token Attacks on Vision Transformers", arXiv, 2021 (New York University). [Paper]
  • Smooth-ViT: "Certified Patch Robustness via Smoothed Vision Transformers", arXiv, 2021 (MIT). [Paper][PyTorch]
  • ?: "Discrete Representations Strengthen Vision Transformer Robustness", arXiv, 2021 (Google). [Paper]
  • ?: "Vision Transformers are Robust Learners", AAAI, 2022 (PyImageSearch + IBM). [Paper][Tensorflow]
  • PNA: "Towards Transferable Adversarial Attacks on Vision Transformers", AAAI, 2022 (Fudan + Maryland). [Paper][PyTorch]
  • MIA-Former: "MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation", AAAI, 2022 (Rice University). [Paper]
  • Patch-Fool: "Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?", ICLR, 2022 (Rice University). [Paper][PyTorch]
  • Generalization-Enhanced-ViT: "Delving Deep into the Generalization of Vision Transformers under Distribution Shifts", CVPR, 2022 (Beihang University + NTU, Singapore). [Paper]
  • ECViT: "Towards Practical Certifiable Patch Defense with Vision Transformer", CVPR, 2022 (Tencent).[Paper]
  • Attention-Fool: "Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness", CVPR, 2022 (Bosch). [Paper]
  • Memory-Token: "Fine-tuning Image Transformers using Learnable Memory", CVPR, 2022 (Google). [Paper]
  • ?: "Are Vision Transformers Robust to Spurious Correlations?", arXiv, 2022 (UW-Madison). [Paper]
  • MA: "Boosting Adversarial Transferability of MLP-Mixer", arXiv, 2022 (Beijing Institute of Technology). [Paper]
  • ?: "Deeper Insights into ViTs Robustness towards Common Corruptions", arXiv, 2022 (Fudan + Microsoft). [Paper]
  • FAN: "Understanding The Robustness in Vision Transformers", arXiv, 2022 (NVIDIA). [Paper]

Model Compression + Transformer

  • ViT-quant: "Post-Training Quantization for Vision Transformer", NeurIPS, 2021 (Huawei). [Paper]
  • VTP: "Visual Transformer Pruning", arXiv, 2021 (Huawei). [Paper]
  • NViT: "NViT: Vision Transformer Compression and Parameter Redistribution", arXiv, 2021 (NVIDIA). [Paper]
  • MD-ViT: "Multi-Dimensional Model Compression of Vision Transformer", arXiv, 2021 (Princeton). [Paper]
  • PTQ4ViT: "PTQ4ViT: Post-Training Quantization Framework for Vision Transformers", arXiv, 2021 (Peking University). [Paper]
  • FQ-ViT: "FQ-ViT: Fully Quantized Vision Transformer without Retraining", arXiv, 2021 (MEGVII). [Paper][PyTorch]
  • UVC: "Unified Visual Transformer Compression", ICLR, 2022 (UT Austin). [Paper][PyTorch]
  • MiniViT: "MiniViT: Compressing Vision Transformers with Weight Multiplexing", CVPR, 2022 (Microsoft). [Paper][PyTorch]
  • Q-ViT: "Q-ViT: Fully Differentiable Quantization for Vision Transformer", arXiv, 2022 (MEGVII). [Paper]
  • VAQF: "VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer", arXiv, 2022 (Northeastern University). [Paper]
  • PSAQ-ViT: "Patch Similarity Aware Data-Free Quantization for Vision Transformers", arXiv, 2022 (CAS). [Paper]
  • VTP: "Vision Transformer Compression with Structured Pruning and Low Rank Approximation", arXiv, 2022 (UCLA). [Paper]
  • SiDT: "Searching Intrinsic Dimensions of Vision Transformers", arXiv, 2022 (UC Irvine). [Paper]

[Back to Overview]

Attention-Free

MLP-Series

  • RepMLP: "RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition", arXiv, 2021 (MEGVII). [Paper][PyTorch]
  • EAMLP: "Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks", arXiv, 2021 (Tsinghua University). [Paper]
  • Forward-Only: "Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet", arXiv, 2021 (Oxford). [Paper][PyTorch]
  • ResMLP: "ResMLP: Feedforward networks for image classification with data-efficient training", arXiv, 2021 (Facebook). [Paper]
  • ?: "Can Attention Enable MLPs To Catch Up With CNNs?", arXiv, 2021 (Tsinghua). [Paper]
  • ViP: "Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition", arXiv, 2021 (NUS, Singapore). [Paper][PyTorch]
  • CCS: "Rethinking Token-Mixing MLP for MLP-based Vision Backbone", arXiv, 2021 (Baidu). [Paper]
  • S2-MLPv2: "S2-MLPv2: Improved Spatial-Shift MLP Architecture for Vision", arXiv, 2021 (Baidu). [Paper]
  • RaftMLP: "RaftMLP: Do MLP-based Models Dream of Winning Over Computer Vision?", arXiv, 2021 (Rikkyo University, Japan). [Paper][PyTorch]
  • Hire-MLP: "Hire-MLP: Vision MLP via Hierarchical Rearrangement", arXiv, 2021 (Huawei). [Paper]
  • Sparse-MLP: "Sparse-MLP: A Fully-MLP Architecture with Conditional Computation", arXiv, 2021 (NUS). [Paper]
  • ConvMLP: "ConvMLP: Hierarchical Convolutional MLPs for Vision", arXiv, 2021 (University of Oregon). [Paper][PyTorch]
  • sMLP: "Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?", arXiv, 2021 (Microsoft). [Paper]
  • MLP-Mixer: "MLP-Mixer: An all-MLP Architecture for Vision", NeurIPS, 2021 (Google). [Paper][Tensorflow][PyTorch-1 (lucidrains)][PyTorch-2 (rishikksh20)]
  • gMLP: "Pay Attention to MLPs", NeurIPS, 2021 (Google). [Paper][PyTorch (antonyvigouret)]
  • S2-MLP: "S2-MLP: Spatial-Shift MLP Architecture for Vision", WACV, 2022 (Baidu). [Paper]
  • CycleMLP: "CycleMLP: A MLP-like Architecture for Dense Prediction", ICLR, 2022 (HKU). [Paper][PyTorch]
  • AS-MLP: "AS-MLP: An Axial Shifted MLP Architecture for Vision", ICLR, 2022 (ShanghaiTech University). [Paper][PyTorch]
  • Wave-MLP: "An Image Patch is a Wave: Quantum Inspired Vision MLP", CVPR, 2022 (Huawei). [Paper][PyTorch]
  • MS-MLP: "Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs", arXiv, 2022 (Microsoft). [Paper]
  • ActiveMLP: "ActiveMLP: An MLP-like Architecture with Active Token Mixer", arXiv, 2022 (Microsoft). [Paper]

Other Attention-Free

  • PoolFormer: "MetaFormer is Actually What You Need for Vision", CVPR, 2022 (Sea AI Lab). [Paper][PyTorch]
  • FocalNet: "Focal Modulation Networks", arXiv, 2022 (Microsoft). [Paper][PyTorch]
  • Sequencer: "Sequencer: Deep LSTM for Image Classification", arXiv, 2022 (Rikkyo University, Japan). [Paper]

[Back to Overview]

Analysis for Transformer

  • Attention-CNN: "On the Relationship between Self-Attention and Convolutional Layers", ICLR, 2020 (EPFL). [Paper][PyTorch][Website]
  • Transformer-Explainability: "Transformer Interpretability Beyond Attention Visualization", CVPR, 2021 (Tel Aviv). [Paper][PyTorch]
  • ?: "Are Convolutional Neural Networks or Transformers more like human vision?", CogSci, 2021 (Princeton). [Paper]
  • ?: "ConvNets vs. Transformers: Whose Visual Representations are More Transferable?", ICCVW, 2021 (HKU). [Paper]
  • ?: "Do Vision Transformers See Like Convolutional Neural Networks?", NeurIPS, 2021 (Google). [Paper]
  • ?: "Intriguing Properties of Vision Transformers", NeurIPS, 2021 (MBZUAI). [Paper][PyTorch]
  • FoveaTer: "FoveaTer: Foveated Transformer for Image Classification", arXiv, 2021 (UCSB). [Paper]
  • ?: "Demystifying Local Vision Transformer: Sparse Connectivity, Weight Sharing, and Dynamic Weight", arXiv, 2021 (Microsoft). [Paper]
  • ?: "Revisiting the Calibration of Modern Neural Networks", arXiv, 2021 (Google). [Paper]
  • ?: "How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers", arXiv, 2021 (Google). [Paper][Tensorflow][PyTorch (rwightman)]
  • ?: "What Makes for Hierarchical Vision Transformer?", arXiv, 2021 (Horizon Robotic). [Paper]
  • ?: "Visualizing Paired Image Similarity in Transformer Networks", WACV, 2022 (Temple University). [Paper][PyTorch]
  • FDSL: "Can Vision Transformers Learn without Natural Images?", AAAI, 2022 (AIST). [Paper][PyTorch][Website]
  • AlterNet: "How Do Vision Transformers Work?", ICLR, 2022 (Yonsei University). [Paper][PyTorch]
  • ?: "When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations", ICLR, 2022 (Google). [Paper][Tensorflow]
  • ?: "On the Connection between Local Attention and Dynamic Depth-wise Convolution", ICLR, 2022 (Microsoft). [Paper]
  • AWD-ViT: "Visualizing and Understanding Patch Interactions in Vision Transformer", arXiv, 2022 (JD). [Paper]
  • ?: "Three things everyone should know about Vision Transformers", arXiv, 2022 (Meta). [Paper]
  • ?: "CNNs and Transformers Perceive Hybrid Images Similar to Humans", arXiv, 2022 (Quintic AI, CA). [Paper][Code]

[Back to Overview]

Detection

Object Detection

  • CNN-based backbone:
    • DETR: "End-to-End Object Detection with Transformers", ECCV, 2020 (Facebook). [Paper][PyTorch]
    • Deformable DETR: "Deformable DETR: Deformable Transformers for End-to-End Object Detection", ICLR, 2021 (SenseTime). [Paper][PyTorch]
    • UP-DETR: "UP-DETR: Unsupervised Pre-training for Object Detection with Transformers", CVPR, 2021 (Tencent). [Paper][PyTorch]
    • SMCA: "Fast Convergence of DETR with Spatially Modulated Co-Attention", ICCV, 2021 (CUHK). [Paper][PyTorch]
    • Conditional-DETR: "Conditional DETR for Fast Training Convergence", ICCV, 2021 (Microsoft). [Paper]
    • PnP-DETR: "PnP-DETR: Towards Efficient Visual Analysis with Transformers", ICCV, 2021 (Yitu). [Paper][Code (in construction)]
    • TSP: "Rethinking Transformer-based Set Prediction for Object Detection", ICCV, 2021 (CMU). [Paper]
    • Dynamic-DETR: "Dynamic DETR: End-to-End Object Detection With Dynamic Attention", ICCV, 2021 (Microsoft). [Paper]
    • ViT-YOLO: "ViT-YOLO:Transformer-Based YOLO for Object Detection", ICCVW, 2021 (Xidian University). [Paper]
    • ACT: "End-to-End Object Detection with Adaptive Clustering Transformer", BMVC, 2021 (Peking + CUHK). [Paper][PyTorch]
    • DIL-ViT: "Paying Attention to Varying Receptive Fields: Object Detection with Atrous Filters and Vision Transformers", BMVC, 2021 (Monash University Malaysia). [Paper]
    • Efficient-DETR: "Efficient DETR: Improving End-to-End Object Detector with Dense Prior", arXiv, 2021 (Megvii). [Paper]
    • CA-FPN: "Content-Augmented Feature Pyramid Network with Light Linear Transformers", arXiv, 2021 (CAS). [Paper]
    • DETReg: "DETReg: Unsupervised Pretraining with Region Priors for Object Detection", arXiv, 2021 (Tel-Aviv + Berkeley). [Paper][Website]
    • GQPos: "Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads", arXiv, 2021 (MEGVII). [Paper]
    • Anchor-DETR: "Anchor DETR: Query Design for Transformer-Based Detector", AAAI, 2022 (MEGVII). [Paper][PyTorch]
    • Sparse-DETR: "Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity", ICLR, 2022 (Kakao). [Paper][PyTorch]
    • DAB-DETR: "DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR", ICLR, 2022 (IDEA, China). [Paper][Code (in construction)]
    • DN-DETR: "DN-DETR: Accelerate DETR Training by Introducing Query DeNoising", CVPR, 2022 (International Digital Economy Academy, China). [Paper][Code (in construction)]
    • SAM-DETR: "Accelerating DETR Convergence via Semantic-Aligned Matching", CVPR, 2022 (NTU, Singapore). [Paper][PyTorch (in construction)]
    • AdaMixer: "AdaMixer: A Fast-Converging Query-Based Object Detector", CVPR, 2022 (Nanjing University). [Paper][Code (in construction)]
    • KA: "Knowledge Amalgamation for Object Detection with Transformers", arXiv, 2022 (Zhejiang University). [Paper]
    • DE-DETR: "Towards Data-Efficient Detection Transformers", arXiv, 2022 (JD). [Paper][Code (in construction)]
    • MIMDet: "Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection", arXiv, 2022 (Tencent). [Paper][PyTorch]
  • Transformer-based backbone:
    • ViT-FRCNN: "Toward Transformer-Based Object Detection", arXiv, 2020 (Pinterest). [Paper]
    • WB-DETR: "WB-DETR: Transformer-Based Detector Without Backbone", ICCV, 2021 (CAS). [Paper]
    • YOLOS: "You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection", NeurIPS, 2021 (Horizon Robotics). [Paper][PyTorch]
    • UViT: "A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation", arXiv, 2021 (Google). [Paper]
    • ?: "Benchmarking Detection Transfer Learning with Vision Transformers", arXiv, 2021 (Facebook). [Paper]
    • ViDT: "ViDT: An Efficient and Effective Fully Transformer-based Object Detector", ICLR, 2022 (NAVER). [Paper][PyTorch]
    • FP-DETR: "FP-DETR: Detection Transformer Advanced by Fully Pre-training", ICLR, 2022 (USTC). [Paper]
    • D2ETR: "D2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention", arXiv, 2022 (Alibaba). [Paper]
    • DINO: "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection", arXiv, 2022 (IDEA, China). [Paper][Code (in construction)]
    • ViTDet: "Exploring Plain Vision Transformer Backbones for Object Detection", arXiv, 2022 (Meta). [Paper]

[Back to Overview]

3D Object Detection

  • AST-GRU: "LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention", CVPR, 2020 (Baidu). [Paper][Code (in construction)]
  • Pointformer: "3D Object Detection with Pointformer", arXiv, 2020 (Tsinghua). [Paper]
  • CT3D: "Improving 3D Object Detection with Channel-wise Transformer", ICCV, 2021 (Alibaba). [Paper][Code (in construction)]
  • Group-Free-3D: "Group-Free 3D Object Detection via Transformers", ICCV, 2021 (Microsoft). [Paper][PyTorch]
  • VoTr: "Voxel Transformer for 3D Object Detection", ICCV, 2021 (CUHK + NUS). [Paper]
  • 3DETR: "An End-to-End Transformer Model for 3D Object Detection", ICCV, 2021 (Facebook). [Paper][PyTorch][Website]
  • DETR3D: "DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries", CoRL, 2021 (MIT). [Paper]
  • M3DETR: "M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers", WACV, 2022 (University of Maryland). [Paper][PyTorch]
  • SST: "Embracing Single Stride 3D Object Detector with Sparse Transformer", CVPR, 2022 (CAS). [Paper][PyTorch]
  • MonoDTR: "MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer", CVPR, 2022 (NTU). [Paper][Code (in construction)]
  • VoxSeT: "Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds", CVPR, 2022 (The Hong Kong Polytechnic University). [Paper][PyTorch]
  • TransFusion: "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers", CVPR, 2022 (HKUST). [Paper][PyTorch]
  • CAT-Det: "CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection", CVPR, 2022 (Beihang University). [Paper]
  • TokenFusion: "Multimodal Token Fusion for Vision Transformers", CVPR, 2022 (Tsinghua). [Paper]
  • PETR: "PETR: Position Embedding Transformation for Multi-View 3D Object Detection", arXiv, 2022 (MEGVII). [Paper]
  • MonoDETR: "MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection", arXiv, 2022 (Shanghai AI Laboratory). [Paper][Code (in construction)]
  • Graph-DETR3D: "Graph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection", arXiv, 2022 (University of Science and Technology of China). [Paper]

[Back to Overview]

Multi-Modal Detection

  • MDETR: "MDETR - Modulated Detection for End-to-End Multi-Modal Understanding", ICCV, 2021 (NYU). [Paper][PyTorch][Website]
  • FETNet: "FETNet: Feature Exchange Transformer Network for RGB-D Object Detection", BMVC, 2021 (Tsinghua). [Paper]
  • MEDUSA: "Exploiting Scene Depth for Object Detection with Multimodal Transformers", BMVC, 2021 (Google). [Paper][PyTorch]
  • StrucTexT: "StrucTexT: Structured Text Understanding with Multi-Modal Transformers", arXiv, 2021 (Baidu). [Paper]
  • simCrossTrans: "simCrossTrans: A Simple Cross-Modality Transfer Learning for Object Detection with ConvNets or Vision Transformers", arXiv, 2022 (The City University of New York). [Paper][PyTorch]
  • X-DETR: "X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks", arXiv, 2022 (Amazon). [Paper]
  • OWL-ViT: "Simple Open-Vocabulary Object Detection with Vision Transformers", arXiv, 2022 (Google). [Paper]

[Back to Overview]

HOI Detection

  • HOI-Transformer: "End-to-End Human Object Interaction Detection with HOI Transformer", CVPR, 2021 (Megvii). [Paper][PyTorch]
  • HOTR: "HOTR: End-to-End Human-Object Interaction Detection with Transformers", CVPR, 2021 (Kakao + Korea University). [Paper][PyTorch]
  • MSTR: "MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection", CVPR, 2022 (Kakao). [Paper]
  • SSRT: "What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions", CVPR, 2022 (Amazon). [Paper]
  • CPC: "Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection", CVPR, 2022 (Korea University). [Paper][Code (in construction)]
  • DisTR: "Human-Object Interaction Detection via Disentangled Transformer", CVPR, 2022 (Baidu). [Paper]
  • Iwin: "Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows", arXiv, 2022 (Shanghai Jiao Tong). [Paper]
  • CATN: "Category-Aware Transformer Network for Better Human-Object Interaction Detection", arXiv, 2022 (Huazhong University of Science and Technology). [Paper]

[Back to Overview]

Salient Object Detection

  • VST: "Visual Saliency Transformer", ICCV, 2021 (Northwestern Polytechincal University). [Paper]
  • ?: "Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction", NeurIPS, 2021 (Baidu). [Paper]
  • SwinNet: "SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection", TCSVT, 2021 (Anhui University). [Paper][Code]
  • SOD-Transformer: "Transformer Transforms Salient Object Detection and Camouflaged Object Detection", arXiv, 2021 (Northwestern Polytechnical University). [Paper]
  • GLSTR: "Unifying Global-Local Representations in Salient Object Detection with Transformer", arXiv, 2021 (South China University of Technology). [Paper]
  • TriTransNet: "TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network", arXiv, 2021 (Anhui University). [Paper]
  • AbiU-Net: "Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net", arXiv, 2021 (Nankai University). [Paper]
  • TranSalNet: "TranSalNet: Visual saliency prediction using transformers", arXiv, 2021 (Cardiff University, UK). [Paper]
  • DFTR: "DFTR: Depth-supervised Hierarchical Feature Fusion Transformer for Salient Object Detection", arXiv, 2022 (Tencent). [Paper]
  • GroupTransNet: "GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection", arXiv, 2022 (Nankai university). [Paper]

[Back to Overview]

Other Detection Tasks

  • X-supervised:
    • LOST: "Localizing Objects with Self-Supervised Transformers and no Labels", BMVC, 2021 (Valeo.ai). [Paper][PyTorch]
    • Omni-DETR: "Omni-DETR: Omni-Supervised Object Detection with Transformers", CVPR, 2022 (Amazon). [Paper]
    • TokenCut: "Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut", arXiv, 2022 (Univ. Grenoble Alpes, France). [Paper][Website]
  • X-Shot Object Detection:
    • AIT: "Adaptive Image Transformer for One-Shot Object Detection", CVPR, 2021 (Academia Sinica). [Paper]
    • Meta-DETR: "Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning", arXiv, 2021 (NTU Singapore). [Paper]
    • CAT: "CAT: Cross-Attention Transformer for One-Shot Object Detection", arXiv, 2021 (Northwestern Polytechnical University). [Paper]
    • FCT: "Few-Shot Object Detection with Fully Cross-Transformer", CVPR, 2022 (Columbia). [Paper]
    • SaFT: "Semantic-aligned Fusion Transformer for One-shot Object Detection", arXiv, 2022 (Microsoft). [Paper]
    • Incremental-DETR: "Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning", arXiv, 2022 (NUS). [Paper]
  • Pedestrian Detection:
    • PED: "DETR for Crowd Pedestrian Detection", arXiv, 2020 (Tsinghua). [Paper][PyTorch]
    • Pedestron: "Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond", arXiv, 2022 (IIAI). [Paper][PyTorch]
  • Lane Detection:
    • LSTR: "End-to-end Lane Shape Prediction with Transformers", WACV, 2021 (Xi'an Jiaotong). [Paper][PyTorch]
    • LETR: "Line Segment Detection Using Transformers without Edges", CVPR, 2021 (UCSD). [Paper][PyTorch]
    • Laneformer: "Laneformer: Object-aware Row-Column Transformers for Lane Detection", AAAI, 2022 (Huawei). [Paper]
    • PersFormer: "PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark", arXiv, 2022 (Shanghai AI Laboratory). [Paper][GitHub]
  • Object Localization:
    • TS-CAM: "TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization", arXiv, 2021 (CAS). [Paper]
    • LCTR: "LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization", AAAI, 2022 (Xiamen University). [Paper]
    • ViTOL: "ViTOL: Vision Transformer for Weakly Supervised Object Localization", CVPRW, 2022 (Mercedes-Benz). [Paper][Code (in construction)]
    • CaFT: "CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization", arXiv, 2022 (Zhejiang University). [Paper]
  • Relation Detection:
    • PST: "Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries", ICCV, 2021 (Amazon). [Paper]
    • RelTransformer: "RelTransformer: Balancing the Visual Relationship Detection from Local Context, Scene and Memory", arXiv, 2021 (KAUST). [Paper][Code (in construction)]
    • PST: "Visual Composite Set Detection Using Part-and-Sum Transformers", arXiv, 2021 (Amazon). [Paper]
    • TROI: "Transformed ROIs for Capturing Visual Transformations in Videos", arXiv, 2021 (NUS, Singapore). [Paper]
  • Anomaly Detection:
    • VT-ADL: "VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization", ISIE, 2021 (University of Udine, Italy). [Paper]
    • InTra: "Inpainting Transformer for Anomaly Detection", arXiv, 2021 (Fujitsu). [Paper]
    • AnoViT: "AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder", arXiv, 2022 (Korea University). [Paper]
  • Domain Adaptation:
    • SSTN: "SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving", arXiv, 2021 (Gwangju Institute of Science and Technology). [Paper]
    • DA-DETR: "DA-DETR: Domain Adaptive Detection Transformer by Hybrid Attention", arXiv, 2021 (NTU Singapore). [Paper]
    • OAA-OTA: "Improving Transferability for Domain Adaptive Detection Transformers", arXiv, 2022 (Beijing Institute of Technology). [Paper]
    • MTKT: "Cross-Domain Object Detection with Mean-Teacher Transformer", arXiv, 2022 (Beihang University). [Paper]
  • Co-Salient Object Detection:
    • CoSformer: "CoSformer: Detecting Co-Salient Object with Transformers", arXiv, 2021 (Nanjing University). [Paper]
  • Oriented Object Detection:
    • O2DETR: "Oriented Object Detection with Transformer", arXiv, 2021 (Baidu). [Paper]
  • Multiview Detection:
    • MVDeTr: "Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)", ACMMM, 2021 (ANU). [Paper]
  • Polygon Detection:
    • ?: "Investigating transformers in the decomposition of polygonal shapes as point collections", ICCVW, 2021 (Delft University of Technology, Netherlands). [Paper]
  • Drone-view:
    • TPH: "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios", ICCVW, 2021 (Beihang University). [Paper]
  • Infrared:
    • ?: "Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds", arXiv, 2021 (Chongqing University of Posts and Telecommunications). [Paper]
  • Text:
    • SwinTextSpotter: "SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition", CVPR, 2022 (South China University of Technology). [Paper][PyTorch]
    • TESTR: "Text Spotting Transformers", CVPR, 2022 (UCSD). [Paper][PyTorch]
    • TTS: "Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer", arXiv, 2022 (Amazon). [Paper]
    • TransDETR: "End-to-End Video Text Spotting with Transformer", arXiv, 2022 (Zhejiang University). [Paper][PyTorch]
    • ?: "Arbitrary Shape Text Detection using Transformers", arXiv, 2022 (University of Waterloo, Canada). [Paper]
    • ?: "Arbitrary Shape Text Detection via Boundary Transformer", arXiv, 2022 (University of Science and Technology Beijing). [Paper][Code (in construction)]
  • Change Detection:
    • ChangeFormer: "A Transformer-Based Siamese Network for Change Detection", arXiv, 2022 (JHU). [Paper][PyTorch]
  • Edge Detection:
  • Person Search:
    • COAT: "Cascade Transformers for End-to-End Person Search", CVPR, 2022 (Kitware). [Paper][PyTorch]
    • PSTR: "PSTR: End-to-End One-Step Person Search With Transformers", CVPR, 2022 (Tianjin University). [Paper][PyTorch]
  • Manipulation Detection:
    • ObjectFormer: "ObjectFormer for Image Manipulation Detection and Localization", arXiv, 2022 (Fudan University). [Paper]
  • Grounded Situation Recognition:
    • CoFormer: "Collaborative Transformers for Grounded Situation Recognition", CVPR, 2022 (POSTECH). [Paper][PyTorch]

[Back to Overview]

Segmentation

Semantic Segmentation

  • SETR: "Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers", CVPR, 2021 (Tencent). [Paper][PyTorch][Website]
  • TrSeg: "TrSeg: Transformer for semantic segmentation", PRL, 2021 (Korea University). [Paper][PyTorch]
  • CWT: "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer", ICCV, 2021 (University of Surrey, UK). [Paper][PyTorch]
  • Segmenter: "Segmenter: Transformer for Semantic Segmentation", ICCV, 2021 (INRIA). [Paper][PyTorch]
  • UN-EPT: "A Unified Efficient Pyramid Transformer for Semantic Segmentation", ICCVW, 2021 (Amazon). [Paper]
  • SegFormer: "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers", NeurIPS, 2021 (NVIDIA). [Paper][PyTorch]
  • FTN: "Fully Transformer Networks for Semantic Image Segmentation", arXiv, 2021 (Baidu). [Paper]
  • OffRoadTranSeg: "OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments", arXiv, 2021 (IISER. India). [Paper]
  • MaskFormer: "Per-Pixel Classification is Not All You Need for Semantic Segmentation", arXiv, 2021 (UIUC + Facebook). [Paper][Website]
  • TRFS: "Boosting Few-shot Semantic Segmentation with Transformers", arXiv, 2021 (ETHZ). [Paper]
  • Flying-Guide-Dog: "Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation", arXiv, 2021 (KIT, Germany). [Paper][Code (in construction)]
  • VSPW: "Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models", arXiv, 2021 (Xiaomi). [Paper]
  • SDTP: "SDTP: Semantic-aware Decoupled Transformer Pyramid for Dense Image Prediction", arXiv, 2021 (?). [Paper]
  • TopFormer: "TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation", CVPR, 2022 (Tencent). [Paper]
  • GroupViT: "GroupViT: Semantic Segmentation Emerges from Text Supervision", CVPR, 2022 (NVIDIA). [Paper][Website][PyTorch]
  • Lawin: "Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention", arXiv, 2022 (Beijing University of Posts and Telecommunications). [Paper][PyTorch]
  • PFT: "Pyramid Fusion Transformer for Semantic Segmentation", arXiv, 2022 (CUHK + SenseTime). [Paper]
  • DFlatFormer: "Dual-Flattening Transformers through Decomposed Row and Column Queries for Semantic Segmentation", arXiv, 2022 (OPPO). [Paper]
  • FeSeFormer: "Feature Selective Transformer for Semantic Image Segmentation", arXiv, 2022 (Baidu). [Paper]
  • StructToken: "StructToken : Rethinking Semantic Segmentation with Structural Prior", arXiv, 2022 (Shanghai AI Lab). [Paper]

[Back to Overview]

Object Segmentation

  • SOTR: "SOTR: Segmenting Objects with Transformers", ICCV, 2021 (China Agricultural University). [Paper][PyTorch]
  • Trans4Trans: "Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World", ICCVW, 2021 (Karlsruhe Institute of Technology, Germany). [Paper][Code (in construction)]
  • Trans2Seg: "Segmenting Transparent Object in the Wild with Transformer", arXiv, 2021 (HKU + SenseTime). [Paper][PyTorch]
  • SOIT: "SOIT: Segmenting Objects with Instance-Aware Transformers", AAAI, 2022 (Hikvision). [Paper][Code (in construction)]

[Back to Overview]

Other Segmentation Tasks

  • Multi-Modal:
    • CMX: "CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers", arXiv, 2022 (Karlsruhe Institute of Technology, Germany). [Paper][PyTorch]
  • Panoptic Segmentation:
    • MaX-DeepLab: "MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers", CVPR, 2021 (Google). [Paper][PyTorch (conradry)]
    • Panoptic-SegFormer: "Panoptic SegFormer", arXiv, 2021 (Nanjing University). [Paper]
    • SIAin: "An End-to-End Trainable Video Panoptic Segmentation Method usingTransformers", arXiv, 2021 (SI Analytics, South Korea). [Paper]
    • VPS-Transformer: "Time-Space Transformers for Video Panoptic Segmentation", WACV, 2022 (Technical University of Cluj-Napoca, Romania). [Paper]
    • Panoptic-PartFormer: "Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation", arXiv, 2022 (Peking). [Paper][Code (in construction)]
  • Depth Estimation & Semantic Segmentation:
    • DPT: "Vision Transformers for Dense Prediction", ICCV, 2021 (Intel). [Paper][PyTorch]
  • Instance Segmentation:
    • ISTR: "ISTR: End-to-End Instance Segmentation with Transformers", arXiv, 2021 (Xiamen University). [Paper][PyTorch]
    • Mask-Transfiner: "Mask Transfiner for High-Quality Instance Segmentation", CVPR, 2022 (ETHZ). [Paper][PyTorch][Website]
  • Depth Estimation:
    • TransDepth: "Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction", ICCV, 2021 (Haerbin Institute of Technology + University of Trento). [Paper][PyTorch]
    • ASTransformer: "Transformer-based Monocular Depth Estimation with Attention Supervision", BMVC, 2021 (USTC). [Paper][PyTorch]
    • MT-SfMLearner: "Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics", VISAP, 2022 (NavInfo Europe, Netherlands). [Paper]
    • DepthFormer: "Multi-Frame Self-Supervised Depth with Transformers", CVPR, 2022 (Toyota). [Paper]
    • GLPanoDepth: "GLPanoDepth: Global-to-Local Panoramic Depth Estimation", arXiv, 2022 (Nanjing University). [Paper]
    • DepthFormer: "DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation", arXiv, 2022 (Harbin Institute of Technology). [Paper][PyTorch]
    • BinsFormer: "BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation", arXiv, 2022 (Harbin Institute of Technology). [Paper][PyTorch]
    • SideRT: "SideRT: A Real-time Pure Transformer Architecture for Single Image Depth Estimation", arXiv, 2022 (Meituan). [Paper]
    • DEST: "Depth Estimation with Simplified Transformer", arXiv, 2022 (NVIDIA). [Paper]
  • Optical Flow:
    • FlowFormer: "FlowFormer: A Transformer Architecture for Optical Flow", arXiv, 2022 (CUHK). [Paper][Website]
  • Panoramic Semantic Segmentation:
    • Trans4PASS: "Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation", CVPR, 2022 (Karlsruhe Institute of Technology, Germany). [Paper][Code (in construction)]
  • X-Shot:
    • CyCTR: "Few-Shot Segmentation via Cycle-Consistent Transformer", NeurIPS, 2021 (University of Technology Sydney). [Paper]
    • LSeg: "Language-driven Semantic Segmentation", ICLR, 2022 (Cornell). [Paper][PyTorch]
    • CATrans: "CATrans: Context and Affinity Transformer for Few-Shot Segmentation", IJCAI, 2022 (Baidu). [Paper]
    • TAFT: "Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation", arXiv, 2022 (KAIST). [Paper]
  • X-Supervised:
    • MCTformer: "Multi-class Token Transformer for Weakly Supervised Semantic Segmentation", CVPR, 2022 (The University of Western Australia). [Paper][Code (in construction)]
    • AFA: "Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers", CVPR, 2022 (Wuhan University). [Paper][PyTorch]
    • HSG: "Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers", CVPR, 2022 (Berkeley). [Paper][PyTorch]
    • TransCAM: "TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentation", arXiv, 2022 (University of Toronto). [Paper][PyTorch]
    • WegFormer: "WegFormer: Transformers for Weakly Supervised Semantic Segmentation", arXiv, 2022 (Tongji University, China). [Paper]
  • Urban Scene:
    • BANet: "Transformer Meets Convolution: A Bilateral Awareness Net-work for Semantic Segmentation of Very Fine Resolution Urban Scene Images", arXiv, 2021 (Wuhan University). [Paper]
  • Crack Detection:
    • CrackFormer: "CrackFormer: Transformer Network for Fine-Grained Crack Detection", ICCV, 2021 (Nanjing University of Science and Technology). [Paper]
  • Camouflaged Object Detection:
    • UGTR: "Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection", ICCV, 2021 (Group42, Abu Dhabi). [Paper][PyTorch]
  • Background Separation:
    • TransBlast: "TransBlast: Self-Supervised Learning Using Augmented Subspace With Transformer for Background/Foreground Separation", ICCVW, 2021 (University of British Columbia). [Paper]
  • Scene Understanding:
    • InvPT: "Inverted Pyramid Multi-task Transformer for Dense Scene Understanding", arXiv, 2022 (HKUST). [Paper]
  • 3D Semantic Segmentation:
    • Stratified-Transformer: "Stratified Transformer for 3D Point Cloud Segmentation", CVPR, 2022 (CUHK). [Paper][PyTorch]
    • CodedVTR: "CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance", arXiv, 2022 (Tsinghua). [Paper]

[Back to Overview]

Video (High-level)

Action Recognition

  • RGB mainly
    • Action Transformer: "Video Action Transformer Network", CVPR, 2019 (DeepMind). [Paper][Code (ppriyank)]
    • ViViT-Ensemble: "Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition", CVPRW, 2021 (Alibaba). [Paper]
    • TimeSformer: "Is Space-Time Attention All You Need for Video Understanding?", ICML, 2021 (Facebook). [Paper][PyTorch (lucidrains)]
    • MViT: "Multiscale Vision Transformers", ICCV, 2021 (Facebook). [Paper][PyTorch]
    • VidTr: "VidTr: Video Transformer Without Convolutions", ICCV, 2021 (Amazon). [Paper][PyTorch]
    • ViViT: "ViViT: A Video Vision Transformer", ICCV, 2021 (Google). [Paper][PyTorch (rishikksh20)]
    • VTN: "Video Transformer Network", ICCVW, 2021 (Theator). [Paper][PyTorch]
    • TokShift: "Token Shift Transformer for Video Classification", ACMMM, 2021 (CUHK). [Paper][PyTorch]
    • Motionformer: "Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers", NeurIPS, 2021 (Facebook). [Paper][PyTorch][Website]
    • X-ViT: "Space-time Mixing Attention for Video Transformer", NeurIPS, 2021 (Samsung). [Paper][PyTorch]
    • SCT: "Shifted Chunk Transformer for Spatio-Temporal Representational Learning", NeurIPS, 2021 (Kuaishou). [Paper]
    • RSANet: "Relational Self-Attention: What's Missing in Attention for Video Understanding", NeurIPS, 2021 (POSTECH). [Paper][PyTorch][Website]
    • STAM: "An Image is Worth 16x16 Words, What is a Video Worth?", arXiv, 2021 (Alibaba). [Paper][Code]
    • GAT: "Enhancing Transformer for Video Understanding Using Gated Multi-Level Attention and Temporal Adversarial Training", arXiv, 2021 (Samsung). [Paper]
    • TokenLearner: "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?", arXiv, 2021 (Google). [Paper]
    • Video-Swin: "Video Swin Transformer", arXiv, 2021 (Microsoft). [Paper][PyTorch]
    • VLF: "VideoLightFormer: Lightweight Action Recognition using Transformers", arXiv, 2021 (The University of Sheffield). [Paper]
    • ORViT: "Object-Region Video Transformers", arXiv, 2021 (Tel Aviv). [Paper][Website]
    • Improved-MViT: "Improved Multiscale Vision Transformers for Classification and Detection", arXiv, 2021 (Meta). [Paper]
    • UniFormer: "UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning", ICLR, 2022 (CAS + SenstTime). [Paper][PyTorch]
    • DirecFormer: "DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition", CVPR, 2022 (University of Arkansas). [Paper][Code (in construction)]
    • AIA: "Attention in Attention: Modeling Context Correlation for Efficient Video Classification", TCSVT, 2022 (University of Science and Technology of China). [Paper][PyTorch]
    • MeMViT: "MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition", arXiv, 2022 (Meta). [Paper]
    • MTV: "Multiview Transformers for Video Recognition", arXiv, 2022 (Google). [Paper]
    • MSCA: "Vision Transformer with Cross-attention by Temporal Shift for Efficient Action Recognition", arXiv, 2022 (Nagoya Institute of Technology). [Paper]
  • Depth
    • Trear: "Trear: Transformer-based RGB-D Egocentric Action Recognition", IEEE Transactions on Cognitive and Developmental Systems, 2021 (Tianjing University). [Paper]
  • Pose:
    • ST-TR: "Spatial Temporal Transformer Network for Skeleton-based Action Recognition", ICPRW, 2020 (Polytechnic University of Milan). [Paper]
    • AcT: "Action Transformer: A Self-Attention Model for Short-Time Human Action Recognition", arXiv, 2021 (Politecnico di Torino, Italy). [Paper][Code (in construction)]
    • STAR: "STAR: Sparse Transformer-based Action Recognition", arXiv, 2021 (UCLA). [Paper]
    • GCsT: "GCsT: Graph Convolutional Skeleton Transformer for Action Recognition", arXiv, 2021 (CAS). [Paper]
    • STTFormer: "Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition", arXiv, 2022 (Xidian University). [Paper][Code (in construction)]
    • ProFormer: "ProFormer: Learning Data-efficient Representations of Body Movement with Prototype-based Feature Augmentation and Visual Transformers", arXiv, 2022 (Karlsruhe Institute of Technology, Germany). [Paper][PyTorch]
  • Multi-modal:
    • MBT: "Attention Bottlenecks for Multimodal Fusion", NeurIPS, 2021 (Google). [Paper]
    • MM-ViT: "MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition", WACV, 2022 (OPPO). [Paper]
    • MMT-NCRC: "Multimodal Transformer for Nursing Activity Recognition", CVPRW, 2022 (UCF). [Paper][Code (in construction)]
    • MVFT: "Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition", arXiv, 2022 (Alibaba). [Paper]
  • Zero-shot:
    • ViSET: "Zero-Shot Action Recognition with Transformer-based Video Semantic Embedding", arXiv, 2022 (University of South FLorida). [Paper]

[Back to Overview]

Action Detection/Localization

  • OadTR: "OadTR: Online Action Detection with Transformers", ICCV, 2021 (Huazhong University of Science and Technology). [Paper][PyTorch]
  • RTD-Net: "Relaxed Transformer Decoders for Direct Action Proposal Generation", ICCV, 2021 (Nanjing University). [Paper][PyTorch]
  • FS-TAL: "Few-Shot Temporal Action Localization with Query Adaptive Transformer", BMVC, 2021 (University of Surrey, UK). [Paper][PyTorch]
  • LSTR: "Long Short-Term Transformer for Online Action Detection", NeurIPS, 2021 (Amazon). [Paper][PyTorch][Website]
  • TubeR: "TubeR: Tube-Transformer for Action Detection", arXiv, 2021 (Amazon). [Paper]
  • ATAG: "Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation", arXiv, 2021 (Alibaba). [Paper]
  • TAPG-Transformer: "Temporal Action Proposal Generation with Transformers", arXiv, 2021 (Harbin Institute of Technology). [Paper]
  • TadTR: "End-to-end Temporal Action Detection with Transformer", arXiv, 2021 (Alibaba). [Paper][Code (in construction)]
  • Vidpress-Soccer: "Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection", arXiv, 2021 (Baidu). [Paper][GitHub]
  • EAMAT: "Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos", IJCAI, 2022 (Beijing Institute of Technology). [Paper][Code (in construction)]
  • CoOadTR: "Continual Transformers: Redundancy-Free Attention for Online Inference", arXiv, 2022 (Aarhus University, Denmark). [Paper][PyTorch]
  • ActionFormer: "ActionFormer: Localizing Moments of Actions with Transformers", arXiv, 2022 (UW-Madison). [Paper][PyTorch]
  • Temporal-Perceiver: "Temporal Perceiver: A General Architecture for Arbitrary Boundary Detection", arXiv, 2022 (Nanjing University). [Paper]
  • LocATe: "LocATe: End-to-end Localization of Actions in 3D with Transformers", arXiv, 2022 (Stanford). [Paper]
  • TALLFormer: "TALLFormer: Temporal Action Localization with Long-memory Transformer", arXiv, 2022 (UNC). [Paper][Code (in construction)]

[Back to Overview]

Action Prediction

  • AVT: "Anticipative Video Transformer", ICCV, 2021 (Facebook). [Paper][PyTorch][Website]
  • HORST: "Higher Order Recurrent Space-Time Transformer", arXiv, 2021 (NVIDIA). [Paper][PyTorch]
  • ?: "Action Forecasting with Feature-wise Self-Attention", arXiv, 2021 (A*STAR). [Paper]
  • ?: "Future Transformer for Long-term Action Anticipation", CVPR, 2022 (POSTECH). [Paper]
  • TTPP: "TTPP: Temporal Transformer with Progressive Prediction for Efficient Action Anticipation", arXiv, 2022 (CAS). [Paper]
  • VPTR: "VPTR: Efficient Transformers for Video Prediction", ICPR, 2022 (Polytechnique Montreal, Canada). [Paper][PyTorch]

[Back to Overview]

Video Object Segmentation

  • SSTVOS: "SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation", CVPR, 2021 (Modiface). [Paper][Code (in construction)]
  • JOINT: "Joint Inductive and Transductive Learning for Video Object Segmentation", ICCV, 2021 (University of Science and Technology of China). [Paper][PyTorch]
  • AOT: "Associating Objects with Transformers for Video Object Segmentation", NeurIPS, 2021 (University of Technology Sydney). [Paper][PyTorch (yoxu515)][Code (in construction)]
  • TransVOS: "TransVOS: Video Object Segmentation with Transformers", arXiv, 2021 (Zhejiang University). [Paper]
  • SITVOS: "Siamese Network with Interactive Transformer for Video Object Segmentation", AAAI, 2022 (JD). [Paper]
  • MTTR: "End-to-End Referring Video Object Segmentation with Multimodal Transformers", CVPR, 2022 (Technion - Israel Institute of Technology). [Paper][PyTorch]
  • AOT: "Associating Objects with Scalable Transformers for Video Object Segmentation", arXiv, 2022 (University of Technology Sydney). [Paper][Code (in construction)]

[Back to Overview]

Video Instance Segmentation

  • VisTR: "End-to-End Video Instance Segmentation with Transformers", CVPR, 2021 (Meituan). [Paper][PyTorch]
  • IFC: "Video Instance Segmentation using Inter-Frame Communication Transformers", NeurIPS, 2021 (Yonsei University). [Paper][PyTorch]
  • Deformable-VisTR: "Deformable VisTR: Spatio temporal deformable attention for video instance segmentation", ICASSP, 2022 (University at Buffalo). [Paper][Code (in construction)]
  • TeViT: "Temporally Efficient Vision Transformer for Video Instance Segmentation", CVPR, 2022 (Tencent). [Paper][PyTorch]
  • MS-STS: "Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer", arXiv, 2022 (MBZUAI). [Paper][Code (in construction)]

[Back to Overview]

Other Video Tasks

  • Action Segmentation
    • ASFormer: "ASFormer: Transformer for Action Segmentation", BMVC, 2021 (Peking University). [Paper][PyTorch]
    • ?: "Transformers in Action: Weakly Supervised Action Segmentation", arXiv, 2022 (TUM). [Paper]
  • Video Object Detection:
    • TransVOD: "End-to-End Video Object Detection with Spatial-Temporal Transformers", arXiv, 2021 (Shanghai Jiao Tong + SenseTime). [Paper][Code (in construction)]
    • MODETR: "MODETR: Moving Object Detection with Transformers", arXiv, 2021 (Valeo, Egypt). [Paper]
    • ST-MTL: "Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation", arXiv, 2021 (Valeo, Egypt). [Paper]
    • ST-DETR: "ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer", arXiv, 2021 (Valeo, Egypt). [Paper]
    • TransVOD: "TransVOD: End-to-end Video Object Detection with Spatial-Temporal Transformers", arXiv, 2022 (Shanghai Jiao Tong + SenseTime). [Paper]
    • ?: "Learning Future Object Prediction with a Spatiotemporal Detection Transformer", arXiv, 2022 (Zenseact, Sweden). [Paper]
  • Video Retrieval
    • SVRTN: "Self-supervised Video Retrieval Transformer Network", arXiv, 2021 (Alibaba). [Paper]
  • Video Hashing
    • BTH: "Self-Supervised Video Hashing via Bidirectional Transformers", CVPR, 2021 (Tsinghua). [Paper][PyTorch]
  • X-supervised Learning:
    • VIMPAC: "VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning", arXiv, 2021 (UNC). [Paper][PyTorch]
    • LSTCL: "Long-Short Temporal Contrastive Learning of Video Transformers", CVPR, 2022 (Facebook). [Paper]
    • SVT: "Self-supervised Video Transformer", CVPR, 2022 (Stony Brook). [Paper][PyTorch][Website]
    • VideoMAE: "VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training", arXiv, 2022 (Tencent). [Paper][Code (in construction)]
  • X-shot:
    • ResT: "Cross-modal Representation Learning for Zero-shot Action Recognition", CVPR, 2022 (Microsoft). [Paper]
  • Anomaly Detection:
    • CT-D2GAN: "Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection", ACMMM, 2021 (NEC). [Paper]
  • Relation Detection:
    • VidVRD: "Video Relation Detection via Tracklet based Visual Transformer", ACMMMW, 2021 (Zhejiang University). [Paper][PyTorch]
  • Saliency Prediction:
    • STSANet: "Spatio-Temporal Self-Attention Network for Video Saliency Prediction", arXiv, 2021 (Shanghai University). [Paper]
    • UFO: "A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection", arXiv, 2022 (South China University of Technology). [Paper][PyTorch]
  • Group Activity:
    • GroupFormer: "GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer", ICCV, 2021 (Sensetime). [Paper]
  • Video Inpainting Detection:
    • FAST: "Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection", ICCV, 2021 (Tsinghua University). [Paper]
  • Driver Activity:
    • TransDARC: "TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration", arXiv, 2022 (Karlsruhe Institute of Technology, Germany). [Paper]
  • Video Alignment:
    • DGWT: "Dynamic Graph Warping Transformer for Video Alignment", BMVC, 2021 (University of New South Wales, Australia). [Paper]
  • Sport-related:
    • Skating-Mixer: "Skating-Mixer: Multimodal MLP for Scoring Figure Skating", arXiv, 2022 (Southern University of Science and Technology). [Paper]
  • Action Counting:
    • TransRAC: "TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting", CVPR, 2022 (ShanghaiTech). [Paper][PyTorch][Website]

[Back to Overview]

Multi-Modality

VQA / Captioning

  • Masked Transformers: "End-to-End Dense Video Captioning with Masked Transformer", CVPR, 2018 (UMich + Salesforce). [Paper][PyTorch]
  • MCAN: "Deep Modular Co-Attention Networks for Visual Question Answering", CVPR, 2019 (Hangzhou Dianzi University). [Paper][PyTorch]
  • ETA-Transformer: "Entangled Transformer for Image Captioning", ICCV, 2019 (UTS). [Paper]
  • M4C: "Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA", CVPR, 2020 (Facebook). [Paper]
  • M2-Transformer: "Meshed-Memory Transformer for Image Captioning", CVPR, 2020 (UniMoRE). [Paper][PyTorch]
  • SA-M4C: "Spatially Aware Multimodal Transformers for TextVQA", ECCV, 2020 (Georgia Tech). [Paper][PyTorch][Website]
  • BMT: "A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer", BMVC, 2020 (Tampere University, Finland). [Paper][PyTorch][Website]
  • CATT: "Causal Attention for Vision-Language Tasks", CVPR, 2021 (NTU Singapore). [Paper][PyTorch]
  • ?: "Optimizing Latency for Online Video CaptioningUsing Audio-Visual Transformers", Interspeech, 2021 (MERL). [Paper]
  • ConClaT: "Contrast and Classify: Training Robust VQA Models", ICCV, 2021 (Georgia Tech). [Paper]
  • MCCFormers: "Describing and Localizing Multiple Changes with Transformers", ICCV, 2021 (AIST). [Paper][Website]
  • TRAR: "TRAR: Routing the Attention Spans in Transformer for Visual Question Answering", ICCV, 2021 (Xiamen University). [Paper]
  • UniQer: "Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue", ICCV, 2021 (Keio). [Paper]
  • SATIC: "Semi-Autoregressive Transformer for Image Captioning", ICCVW, 2021 (Hefei University of Technology). [Paper][PyTorch]
  • DGCN: "Dual Graph Convolutional Networks with Transformer and Curriculum Learning for Image Captioning", ACMMM, 2021 (Wuhan University). [Paper]
  • TxT: "TxT: Crossmodal End-to-End Learning with Transformers", GCPR, 2021 (TU Darmstadt). [Paper]
  • ProTo: "ProTo: Program-Guided Transformer for Program-Guided Tasks", NeurIPS, 2021 (Georiga Tech). [Paper]
  • CPTR: "CPTR: Full Transformer Network for Image Captioning", arXiv, 2021 (CAS). [Paper]
  • VisQA: "VisQA: X-raying Vision and Language Reasoning in Transformers", arXiv, 2021 (INSA-Lyon). [Paper][PyTorch]
  • ReFormer: "ReFormer: The Relational Transformer for Image Captioning", arXiv, 2021 (Stony Brook University). [Paper]
  • ?: "Mounting Video Metadata on Transformer-based Language Model for Open-ended Video Question Answering", arXiv, 2021 (Seoul National University). [Paper]
  • LAViTeR: "LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation", arXiv, 2021 (University at Buffalo). [Paper]
  • TPT: "Temporal Pyramid Transformer with Multimodal Interaction for Video Question Answering", arXiv, 2021 (CAS). [Paper]
  • LATGeO: "Label-Attention Transformer with Geometrically Coherent Objects for Image Captioning", arXiv, 2021 (Gwangju Institute of Science and Technology). [Paper]
  • GEVST: "Geometry-Entangled Visual Semantic Transformer for Image Captioning", arXiv, 2021 (NTU, Singapore). [Paper]
  • GAT: "Geometry Attention Transformer with Position-aware LSTMs for Image Captioning", arXiv, 2021 (University of Electronic Science and Technology of China). [Paper]
  • Block-Skim: "Block-Skim: Efficient Question Answering for Transformer", AAAI, 2022 (* Shanghai Jiao Tong*). [Paper]
  • PureT: "End-to-End Transformer Based Model for Image Captioning", AAAI, 2022 (CAS). [Paper]
  • RelViT: "RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning", ICLR, 2022 (NVIDIA). [Paper]
  • Hypergraph-Transformer: "Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering", ACL, 2022 (SNU). [Paper][Code (in construction)]
  • X-Trans2Cap: "X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning", CVPR, 2022 (CUHK). [Paper]
  • SwinBERT: "SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning", CVPR, 2022 (Microsoft). [Paper]
  • UTC: "UTC: A Unified Transformer with Inter-Task Contrastive Learning for Visual Dialog", CVPR, 2022 (Fudan). [Paper]
  • SpaCap3D: "Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds", IJCAI, 2022 (University of Sydney). [Paper][Code (in construction)][Website]
  • UCM: "Self-Training Vision Language BERTs with a Unified Conditional Model", arXiv, 2022 (NTU, Singapore). [Paper]
  • ViNTER: "ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer", arXiv, 2022 (The University of Tokyo). [Paper]
  • TMN: "Transformer Module Networks for Systematic Generalization in Visual Question Answering", arXiv, 2022 (Fujitsu). [Paper]
  • ?: "On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering", arXiv, 2022 (Birla Institute of Technology Mesra, India). [Paper]
  • DST: "Towards Efficient and Elastic Visual Question Answering with Doubly Slimmable Transformer", arXiv, 2022 (Hangzhou Dianzi University). [Paper]
  • PAVCR: "Attention Mechanism based Cognition-level Scene Understanding", arXiv, 2022 (Leibniz University of Hannover, Germany). [Paper]
  • LW-Transformer: "Towards Lightweight Transformer via Group-wise Transformation for Vision-and-Language Tasks", arXiv, 2022 (Xiamen University). [Paper][PyTorch]
  • D2: "Dual-Level Decoupled Transformer for Video Captioning", arXiv, 2022 (Northwestern Polytechnical University, China). [Paper]

[Back to Overview]

Visual Grounding

  • Multi-Stage-Transformer: "Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos", CVPR, 2021 (University of Electronic Science and Technology of China). [Paper]
  • TransRefer3D: "TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding", ACMMM, 2021 (Beihang University). [Paper]
  • ?: "Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers", EMNLP, 2021 (University of Trento). [Paper]
  • GTR: "On Pursuit of Designing Multi-modal Transformer for Video Grounding", EMNLP, 2021 (Peking). [Paper]
  • MITVG: "Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation", ACL Findings, 2021 (Tencent). [Paper]
  • STVGBert: "STVGBert: A Visual-Linguistic Transformer Based Framework for Spatio-Temporal Video Grounding", ICCV, 2021 (Tencent). [Paper]
  • TransVG: "TransVG: End-to-End Visual Grounding with Transformers", ICCV, 2021 (USTC). [Paper]
  • GSRTR: "Grounded Situation Recognition with Transformers", BMVC, 2021 (POSTECH). [Paper][PyTorch]
  • DRFT: "End-to-end Multi-modal Video Temporal Grounding", NeurIPS, 2021 (UC Merced). [Paper]
  • Referring-Transformer: "Referring Transformer: A One-step Approach to Multi-task Visual Grounding", NeurIPS, 2021 (UBC). [Paper]
  • VGTR: "Visual Grounding with Transformers", arXiv, 2021 (Beihang University). [Paper]
  • Word2Pix: "Word2Pix: Word to Pixel Cross Attention Transformer in Visual Grounding", arXiv, 2021 (A*STAR). [Paper]
  • TubeDETR: "TubeDETR: Spatio-Temporal Video Grounding with Transformers", CVPR, 2022 (INRIA). [Paper][Website]
  • MVT: "Multi-View Transformer for 3D Visual Grounding", CVPR, 2022 (CUHK). [Paper][PyTorch]
  • VidGTR: "Explore and Match: End-to-End Video Grounding with Transformer", arXiv, 2022 (KAIST). [Paper]
  • SeqTR: "SeqTR: A Simple yet Universal Network for Visual Grounding", arXiv, 2022 (Xiamen University). [Paper][Code (in construction)]

[Back to Overview]

Multi-Modal Representation Learning

  • LXMERT: "LXMERT: Learning Cross-Modality Encoder Representations from Transformers", EMNLP, 2019 (UNC). [Paper][PyTorch]
  • ViLBERT: "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks", NeurIPS, 2019 (GaTech). [Paper][PyTorch]
  • Unified-VLP: "Unified Vision-Language Pre-Training for Image Captioning and VQA", AAAI, 2020 (UMich + Microsoft). [Paper][PyTorch]
  • UNITER: "UNITER: UNiversal Image-TExt Representation Learning", ECCV, 2020 (Microsoft). [Paper][PyTorch]
  • COOT: "COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning", NeurIPS, 2020 (University of Freiburg). [Paper][PyTorch]
  • Parameter-Reduction: "Parameter Efficient Multimodal Transformers for Video Representation Learning", ICLR, 2021 (Seoul National University). [Paper]
  • VinVL: "VinVL: Revisiting Visual Representations in Vision-Language Models", CVPR, 2021 (Microsoft). [Paper][Code]
  • CLIP: "Learning Transferable Visual Models From Natural Language Supervision", ICML, 2021 (OpenAI). [Paper][PyTorch]
  • ViLT: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision", ICML, 2021 (Kakao). [Paper][PyTorch]
  • VML: "VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding", ACL Findings, 2021 (Facebook). [Paper]
  • VATT: "VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text", NeurIPS, 2021 (Google). [Paper][Tensorflow]
  • SVO-Probes: "Probing Image-Language Transformers for Verb Understanding", arXiv, 2021 (DeepMind). [Paper]
  • CLIP-ViL: "How Much Can CLIP Benefit Vision-and-Language Tasks?", arXiv, 2021 (Berkeley + UCLA). [Paper][PyTorch]
  • ?: "Survey: Transformer based Video-Language Pre-training", arXiv, 2021 (Renmin University of China). [Paper]
  • TAN: "Temporal Alignment Networks for Long-term Video", CVPR, 2022 (Oxford). [Paper][Code (in construction)][Website]
  • LiT: "LiT: Zero-Shot Transfer with Locked-image text Tuning", CVPR, 2022 (Google). [Paper]
  • Omnivore: "Omnivore: A Single Model for Many Visual Modalities", arXiv, 2022 (Meta). [Paper][PyTorch]
  • MultiMAE: "MultiMAE: Multi-modal Multi-task Masked Autoencoders", arXiv, 2022 (EPFL). [Paper][PyTorch][Website]
  • Flamingo: "Flamingo: a Visual Language Model for Few-Shot Learning", arXiv, 2022 (DeepMind). [Paper]
  • PyramidCLIP: "PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining", arXiv, 2022 (Tencent). [Paper]
  • CoCa: "CoCa: Contrastive Captioners are Image-Text Foundation Models", arXiv, 2022 (Google). [Paper]

[Back to Overview]

Multi-Modal Retrieval

  • MMT: "Multi-modal Transformer for Video Retrieval", ECCV, 2020 (INRIA + Google). [Paper][Website]
  • Fast-and-Slow: "Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers", CVPR, 2021 (DeepMind). [Paper]
  • HTR: "Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning", CVPR, 2021 (Amazon). [Paper][PyTorch]
  • ClipBERT: "Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling", CVPR, 2021 (UNC + Microsoft). [Paper][PyTorch]
  • AYCE: "All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers", CVPRW, 2021 (University of Modena and Reggio Emilia). [Paper][PyTorch]
  • TERN: "Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features", CBMI, 2021 (National Research Council, Italy). [Paper]
  • HiT: "HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval", ICCV, 2021 (Kuaishou). [Paper]
  • VisualSparta: "VisualSparta: Sparse Transformer Fragment-level Matching for Large-scale Text-to-Image Search", arXiv, 2021 (CMU). [Paper]
  • WebVid-2M: "Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval", arXiv, 2021 (Oxford). [Paper]
  • CCR-CCS: "More Than Just Attention: Learning Cross-Modal Attentions with Contrastive Constraints", arXiv, 2021 (Rutgers + Amazon). [Paper]
  • UMT: "UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection", CVPR, 2022 (Tencent). [Paper][Code (in constrcution)]
  • CenterCLIP: "CenterCLIP: Token Clustering for Efficient Text-Video Retrieval", SIGIR, 2022 (Zhejiang University). [Paper]
  • BridgeFormer: "BridgeFormer: Bridging Video-text Retrieval with Multiple Choice Questions", arXiv, 2022 (HKU). [Paper][Website]
  • LoopITR: "LoopITR: Combining Dual and Cross Encoder Architectures for Image-Text Retrieval", arXiv, 2022 (UNC). [Paper]
  • MDMMT-2: "MDMMT-2: Multidomain Multimodal Transformer for Video Retrieval, One More Step Towards Generalization", arXiv, 2022 (Huawei). [Paper]
  • MILES: "MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrieval", arXiv, 2022 (HKU). [Paper]
  • TNLBT: "Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training", arXiv, 2022 (The University of Electro-Communications, Japan). [Paper]

[Back to Overview]

Multi-Modal Generation

  • DALL-E: "Zero-Shot Text-to-Image Generation", ICML, 2021 (OpenAI). [Paper][PyTorch][PyTorch (lucidrains)]
  • CogView: "CogView: Mastering Text-to-Image Generation via Transformers", NeurIPS, 2021 (Tsinghua). [Paper][PyTorch][Website]
  • DALL-Eval: "DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers", arXiv, 2022 (UNC). [Paper][PyTorch]
  • DALL-E-2: "Hierarchical Text-Conditional Image Generation with CLIP Latents", arXiv, 2022 (OpenAI). [Paper][Website]
  • CogView2: "CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers", arXiv, 2022 (Tsinghua). [Paper]
  • ?: "A very preliminary analysis of DALL-E 2", arXiv, 2022 (NYU). [Paper]

[Back to Overview]

Visual Document Understanding

  • LayoutLMv2: "LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding", ACL, 2021 (Microsoft). [Paper][PyTorch]
  • DocFormer: "DocFormer: End-to-End Transformer for Document Understanding", ICCV, 2021 (Amazon). [Paper]
  • TableFormer: "TableFormer: Table Structure Understanding with Transformers", arXiv, 2022 (IBM). [Paper]
  • DocEnTr: "DocEnTr: An End-to-End Document Image Enhancement Transformer", arXiv, 2022 (UAB, Spain). [Paper][PyTorch]
  • DocSegTr: "DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer", arXiv, 2022 (UAB, Spain). [Paper]
  • DiT: "DiT: Self-supervised Pre-training for Document Image Transformer", arXiv, 2022 (Microsoft). [Paper][Code (in construction)]
  • LayoutLMv3: "LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking", arXiv, 2022 (Microsoft). [Paper][PyTorch]

[Back to Overview]

Scene Graph

  • BGT-Net: "BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation", CVPRW, 2021 (ETHZ). [Paper]
  • STTran: "Spatial-Temporal Transformer for Dynamic Scene Graph Generation", ICCV, 2021 (Leibniz University Hannover, Germany). [Paper][PyTorch]
  • SGG-NLS: "Learning to Generate Scene Graph from Natural Language Supervision", ICCV, 2021 (University of Wisconsin-Madison). [Paper][PyTorch]
  • SGG-Seq2Seq: "Context-Aware Scene Graph Generation With Seq2Seq Transformers", ICCV, 2021 (Layer 6 AI, Canada). [Paper][PyTorch]
  • RELAX: "Image-Text Alignment using Adaptive Cross-attention with Transformer Encoder for Scene Graphs", BMVC, 2021 (Samsung). [Paper]
  • Relation-Transformer: "Scenes and Surroundings: Scene Graph Generation using Relation Transformer", arXiv, 2021 (LMU Munich). [Paper]
  • SGTR: "SGTR: End-to-end Scene Graph Generation with Transformer", CVPR, 2022 (ShanghaiTech). [Paper]
  • RelTR: "RelTR: Relation Transformer for Scene Graph Generation", arXiv, 2022 (Leibniz University Hannover, Germany). [Paper][PyTorch]

[Back to Overview]

Other Multi-Modal Tasks

  • Segmentation:
    • VLT: "Vision-Language Transformer and Query Generation for Referring Segmentation", ICCV, 2021 (NTU, Singapore). [Paper][Tensorflow]
  • Analysis:
    • MM-Explainability: "Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers", ICCV, 2021 (Tel Aviv). [Paper][PyTorch]
  • Speaker Localization:
    • ?: "The Right to Talk: An Audio-Visual Transformer Approach", ICCV, 2021 (University of Arkansas). [Paper]
  • Multi-task:
    • UniT: "Transformer is All You Need: Multimodal Multitask Learning with a Unified Transformer", ICCV, 2021 (Facebook). [Paper][PyTorch][Website]
  • Language-based Video Editing:
    • M3L: "Language-based Video Editing via Multi-Modal Multi-Level Transformer", CVPR, 2022 (UCSB). [Paper]
  • Video Summarization:
    • GPT2MVS: "GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization", ICMR, 2021 (BBC). [Paper]
    • QVHighlights: "QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries", NeurIPS, 2021 (UNC). [Paper][PyTorch]
    • HMT: "Hierarchical Multimodal Transformer to Summarize Videos", arXiv, 2021 (Xidian University). [Paper]
  • Robotics:
    • CRT: "Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions", IROS, 2021 (Keio University). [Paper]
    • TraSeTR: "TraSeTR: Track-to-Segment Transformer with Contrastive Query for Instance-level Instrument Segmentation in Robotic Surgery", ICRA, 2022 (CUHK). [Paper]
  • Multi-modal Fusion:
    • MICA: "Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion", ICCV, 2021 (Southwest Jiaotong University). [Paper]
    • IFT: "Image Fusion Transformer", arXiv, 2021 (Johns Hopkins). [Paper][PyTorch]
    • PPT: "PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion", arXiv, 2021 (?). [Paper]
    • TransFuse: "TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning", arXiv, 2022 (Fudan University). [Paper]
    • SwinFuse: "SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images", arXiv, 2022 (Taiyuan University of Science and Technology). [Paper]
  • Human Interaction:
    • Dyadformer: "Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactions", ICCVW, 2021 (Universitat de Barcelona). [Paper]
  • Sign Language Translation:
    • LWTA: "Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation", ICCV, 2021 (Cyprus University of Technology). [Paper]
  • 3D Object Identification:
    • 3DRefTransformer: "3DRefTransformer: Fine-Grained Object Identification in Real-World Scenes Using Natural Language", WACV, 2022 (KAUST). [Paper][Website]
  • Speech Recognition:
    • AV-HuBERT: "Robust Self-Supervised Audio-Visual Speech Recognition", arXiv, 2022 (Meta). [Paper][PyTorch]
    • ?: "Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition", arXiv, 2022 (Google). [Paper]
  • Emotion Recognition:
    • ?: "A Pre-trained Audio-Visual Transformer for Emotion Recognition", ICASSP, 2022 (USC). [Paper]
  • Voice Separation:
    • VoViT: "VoViT: Low Latency Graph-based Audio-Visual Voice Separation Transformer", arXiv, 2022 (Universitat Pompeu Fabra, Spain). [Paper][Website]
  • Language-guided Video Segmentation:
    • Locater: "Local-Global Context Aware Transformer for Language-Guided Video Segmentation", arXiv, 2022 (Zhejiang). [Paper][PyTorch]
  • Analysis:
    • ?: "Are Multimodal Transformers Robust to Missing Modality?", CVPR, 2022 (University of Delaware). [Paper]
    • VL-InterpreT: "VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers", CVPR (demo), 2022 (Intel). [Paper][Website][Video]

[Back to Overview]

Other High-level Vision Tasks

Point Cloud

  • PCT: "PCT: Point Cloud Transformer", arXiv, 2020 (Tsinghua). [Paper][Jittor][PyTorch (uyzhang)]
  • Point-Transformer: "Point Transformer", arXiv, 2020 (Ulm University). [Paper]
  • NDT-Transformer: "NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation", ICRA, 2021 (University of Sheffield). [Paper][PyTorch]
  • P4Transformer: "Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos", CVPR, 2021 (NUS). [Paper]
  • PTT: "PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds", IROS, 2021 (Northeastern University). [Paper][PyTorch (in construction)]
  • SnowflakeNet: "SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer", ICCV, 2021 (Tsinghua). [Paper][PyTorch]
  • PoinTr: "PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers", ICCV, 2021 (Tsinghua). [Paper][PyTorch]
  • Point-Transformer: "Point Transformer", ICCV, 2021 (Oxford + CUHK). [Paper][PyTorch (lucidrains)]
  • CT: "Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks", ICCV, 2021 (Samsung). [Paper]
  • 3DVG-Transformer: "3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds", ICCV, 2021 (Beihang University). [Paper]
  • PPT-Net: "Pyramid Point Cloud Transformer for Large-Scale Place Recognition", ICCV, 2021 (Nanjing University of Science and Technology). [Paper]
  • LTTR: "3D Object Tracking with Transformer", BMVC, 2021 (Northeastern University, China). [Paper][Code (in construction)]
  • ?: "Shape registration in the time of transformers", NeurIPS, 2021 (Sapienza University of Rome). [Paper]
  • YOGO: "You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module", arXiv, 2021 (Berkeley). [Paper][PyTorch]
  • DTNet: "Dual Transformer for Point Cloud Analysis", arXiv, 2021 (Southwest University). [Paper]
  • MLMSPT: "Point Cloud Learning with Transformer", arXiv, 2021 (Southwest University). [Paper]
  • PQ-Transformer: "PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds", arXiv, 2021 (Tsinghua). [Paper][PyTorch]
  • PST2: "Spatial-Temporal Transformer for 3D Point Cloud Sequences", WACV, 2022 (Sun Yat-sen University). [Paper]
  • SCTN: "SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation", AAAI, 2022 (KAUST). [Paper]
  • AWT-Net: "Adaptive Wavelet Transformer Network for 3D Shape Representation Learning", ICLR, 2022 (NYU). [Paper]
  • ?: "Deep Point Cloud Reconstruction", ICLR, 2022 (KAIST). [Paper]
  • HiTPR: "HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud", ICRA, 2022 (Nanjing University of Science and Technology). [Paper]
  • FastPointTransformer: "Fast Point Transformer", CVPR, 2022 (POSTECH). [Paper]
  • REGTR: "REGTR: End-to-end Point Cloud Correspondences with Transformers", CVPR, 2022 (NUS, Singapore). [Paper]
  • GeoTransformer: "Geometric Transformer for Fast and Robust Point Cloud Registration", arXiv, 2022 (National University of Defense Technology, China). [Paper][PyTorch]
  • LighTN: "LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsampling", arXiv, 2022 (Beijing Jiaotong University). [Paper]
  • PMP-Net++: "PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths", arXiv, 2022 (Tsinghua). [Paper]
  • SnowflakeNet: "Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer", arXiv, 2022 (Tsinghua). [Paper][PyTorch]
  • ShapeFormer: "ShapeFormer: Transformer-based Shape Completion via Sparse Representation", arXiv, 2022 (Shenzhen University). [Paper][Website]
  • 3DCTN: "3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification", arXiv, 2022 (University of Waterloo, Canada). [Paper]

[Back to Overview]

Pose Estimation

  • Human-related:
    • Hand-Transformer: "Hand-Transformer: Non-Autoregressive Structured Modeling for 3D Hand Pose Estimation", ECCV, 2020 (Kwai). [Paper]
    • HOT-Net: "HOT-Net: Non-Autoregressive Transformer for 3D Hand-Object Pose Estimation", ACMMM. 2020 (Kwai). [Paper]
    • TransPose: "TransPose: Towards Explainable Human Pose Estimation by Transformer", arXiv, 2020 (Southeast University). [Paper][PyTorch]
    • PTF: "Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration", CVPR, 2021 (ETHZ). [Paper][Code (in construction)][Website]
    • METRO: "End-to-End Human Pose and Mesh Reconstruction with Transformers", CVPR, 2021 (Microsoft). [Paper][PyTorch]
    • PRTR: "Pose Recognition with Cascade Transformers", CVPR, 2021 (UCSD). [Paper][PyTorch]
    • Mesh-Graphormer: "Mesh Graphormer", ICCV, 2021 (Microsoft). [Paper][PyTorch]
    • THUNDR: "THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers", ICCV, 2021 (Google). [Paper]
    • PoseFormer: "3D Human Pose Estimation with Spatial and Temporal Transformers", ICCV, 2021 (UNC). [Paper][PyTorch]
    • TransPose: "TransPose: Keypoint Localization via Transformer", ICCV, 2021 (Southeast University, China). [Paper][PyTorch]
    • SCAT: "SCAT: Stride Consistency With Auto-Regressive Regressor and Transformer for Hand Pose Estimation", ICCVW, 2021 (Alibaba). [Paper]
    • POTR: "Pose Transformers (POTR): Human Motion Prediction With Non-Autoregressive Transformers", ICCVW, 2021 (Idiap). [Paper]
    • TransFusion: "TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation", BMVC, 2021 (UC Irvine). [Paper][PyTorch]
    • HRT: "HRFormer: High-Resolution Transformer for Dense Prediction", NeurIPS, 2021 (CAS). [Paper][PyTorch]
    • POET: "End-to-End Trainable Multi-Instance Pose Estimation with Transformers", arXiv, 2021 (EPFL). [Paper]
    • Lifting-Transformer: "Lifting Transformer for 3D Human Pose Estimation in Video", arXiv, 2021 (Peking). [Paper]
    • TFPose: "TFPose: Direct Human Pose Estimation with Transformers", arXiv, 2021 (The University of Adelaide). [Paper][PyTorch]
    • Skeletor: "Skeletor: Skeletal Transformers for Robust Body-Pose Estimation", arXiv, 2021 (University of Surrey). [Paper]
    • HandsFormer: "HandsFormer: Keypoint Transformer for Monocular 3D Pose Estimation of Hands and Object in Interaction", arXiv, 2021 (Graz University of Technology). [Paper]
    • TTP: "Test-Time Personalization with a Transformer for Human Pose Estimation", NeurIPS, 2021 (UCSD). [Paper][PyTorch][Website]
    • GraFormer: "GraFormer: Graph Convolution Transformer for 3D Pose Estimation", arXiv, 2021 (CAS). [Paper]
    • GCT: "Geometry-Contrastive Transformer for Generalized 3D Pose Transfer", AAAI, 2022 (University of Oulu). [Paper][PyTorch]
    • MHFormer: "MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation", CVPR, 2022 (Peking). [Paper][PyTorch]
    • PAHMT: "Spatial-Temporal Parallel Transformer for Arm-Hand Dynamic Estimation", CVPR, 2022 (NetEase). [Paper]
    • TCFormer: "Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer", CVPR, 2022 (CUHK). [Paper][Code (in construction)]
    • AggPose: "AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation", IJCAI, 2022 (Shenzhen Baoan Women’s and Childiren’s Hospital). [Paper][Code (in construction)]
    • Swin-Pose: "Swin-Pose: Swin Transformer Based Human Pose Estimation", arXiv, 2022 (UMass Lowell) [Paper]
    • Poseur: "Poseur: Direct Human Pose Regression with Transformers", arXiv, 2022 (The University of Adelaide, Australia). [Paper]
    • HeadPosr: "HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders", arXiv, 2022 (ETHZ). [Paper]
    • CrossFormer: "CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose Estimation", arXiv, 2022 (Canberra University, Australia). [Paper]
    • ViTPose: "ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation", arXiv, 2022 (The University of Sydney). [Paper][PyTorch]
  • Others:
    • TAPE: "Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry", arXiv, 2020 (Tianjing University). [Paper]
    • T6D-Direct: "T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression", GCPR, 2021 (University of Bonn). [Paper]
    • 6D-ViT: "6D-ViT: Category-Level 6D Object Pose Estimation via Transformer-based Instance Representation Learning", arXiv, 2021 (University of Science and Technology of China). [Paper]

[Back to Overview]

Tracking

  • TransTrack: "TransTrack: Multiple-Object Tracking with Transformer",arXiv, 2020 (HKU + ByteDance) . [Paper][PyTorch]
  • TransformerTrack: "Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking", CVPR, 2021 (USTC). [Paper][PyTorch]
  • TransT: "Transformer Tracking", CVPR, 2021 (Dalian University of Technology). [Paper][PyTorch]
  • STARK: "Learning Spatio-Temporal Transformer for Visual Tracking", ICCV, 2021 (Microsoft). [Paper][PyTorch]
  • HiFT: "HiFT: Hierarchical Feature Transformer for Aerial Tracking", ICCV, 2021 (Tongji University). [Paper][PyTorch]
  • DTT: "High-Performance Discriminative Tracking With Transformers", ICCV, 2021 (CAS). [Paper]
  • DualTFR: "Learning Tracking Representations via Dual-Branch Fully Transformer Networks", ICCVW, 2021 (Microsoft). [Paper][PyTorch (in construction)]
  • TrackFormer: "TrackFormer: Multi-Object Tracking with Transformers", arXiv, 2021 (Facebook). [Paper]
  • TransCenter: "TransCenter: Transformers with Dense Queries for Multiple-Object Tracking", arXiv, 2021 (INRIA + MIT). [Paper]
  • TransMOT: "TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking", arXiv, 2021 (Microsoft). [Paper]
  • TREG: "Target Transformed Regression for Accurate Tracking", arXiv, 2021 (Nanjing University). [Paper][Code (in construction)]
  • MOTR: "MOTR: End-to-End Multiple-Object Tracking with TRansformer", arXiv, 2021 (MEGVII). [Paper][PyTorch]
  • TrTr: "TrTr: Visual Tracking with Transformer", arXiv, 2021 (University of Tokyo). [Paper][Pytorch]
  • RelationTrack: "RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation", arXiv, 2021 (Huazhong Univerisity of Science and Technology). [Paper]
  • SiamTPN: "Siamese Transformer Pyramid Networks for Real-Time UAV Tracking", WACV, 2022 (New York University). [Paper]
  • MixFormer: "MixFormer: End-to-End Tracking with Iterative Mixed Attention", CVPR, 2022 (Nanjing University). [Paper][PyTorch]
  • ToMP: "Transforming Model Prediction for Tracking", CVPR, 2022 (ETHZ). [Paper][PyTorch]
  • GTR: "Global Tracking Transformers", CVPR, 2022 (UT Austin). [Paper][PyTorch]
  • UTT: "Unified Transformer Tracker for Object Tracking", CVPR, 2022 (Meta). [Paper][Code (in construction)]
  • MeMOT: "MeMOT: Multi-Object Tracking with Memory", CVPR, 2022 (Amazon). [Paper]
  • CSwinTT: "Transformer Tracking with Cyclic Shifting Window Attention", CVPR, 2022 (Huazhong University of Science and Technology). [Paper][PyTorch]
  • SparseTT: "SparseTT: Visual Tracking with Sparse Transformers", IJCAI, 2022 (Beihang University). [Paper][Code (in construction)]
  • TransT-M: "High-Performance Transformer Tracking", arXiv, 2022 (Dalian University of Technology). [Paper]
  • HCAT: "Efficient Visual Tracking via Hierarchical Cross-Attention Transformer", arXiv, 2022 (Dalian University of Technology). [Paper]
  • ?: "Keypoints Tracking via Transformer Networks", arXiv, 2022 (KAIST). [Paper][PyTorch]

[Back to Overview]

Re-ID

  • PAT: "Diverse Part Discovery: Occluded Person Re-Identification With Part-Aware Transformer", CVPR, 2021 (University of Science and Technology of China). [Paper]
  • HAT: "HAT: Hierarchical Aggregation Transformers for Person Re-identification", ACMMM, 2021 (Dalian University of Technology). [Paper]
  • TransReID: "TransReID: Transformer-based Object Re-Identification", ICCV, 2021 (Alibaba). [Paper][PyTorch]
  • APD: "Transformer Meets Part Model: Adaptive Part Division for Person Re-Identification", ICCVW, 2021 (Meituan). [Paper]
  • Pirt: "Pose-guided Inter- and Intra-part Relational Transformer for Occluded Person Re-Identification", ACMMM, 2021 (Beihang University). [Paper]
  • TransMatcher: "Transformer-Based Deep Image Matching for Generalizable Person Re-identification", NeurIPS, 2021 (IIAI). [Paper][PyTorch]
  • STT: "Spatiotemporal Transformer for Video-based Person Re-identification", arXiv, 2021 (Beihang University). [Paper]
  • AAformer: "AAformer: Auto-Aligned Transformer for Person Re-Identification", arXiv, 2021 (CAS). [Paper]
  • TMT: "A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification", arXiv, 2021 (Dalian University of Technology). [Paper]
  • LA-Transformer: "Person Re-Identification with a Locally Aware Transformer", arXiv, 2021 (University of Maryland Baltimore County). [Paper]
  • DRL-Net: "Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification", arXiv, 2021 (Peking University). [Paper]
  • GiT: "GiT: Graph Interactive Transformer for Vehicle Re-identification", arXiv, 2021 (Huaqiao University). [Paper]
  • OH-Former: "OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification", arXiv, 2021 (Shanghaitech University). [Paper]
  • CMTR: "CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification", arXiv, 2021 (Beijing Jiaotong University). [Paper]
  • PFD: "Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer", AAAI, 2022 (Peking). [Paper][PyTorch]
  • NFormer: "NFormer: Robust Person Re-identification with Neighbor Transformer", CVPR, 2022 (University of Amsterdam, Netherlands). [Paper][Code (in construction)]
  • PiT: "Multi-direction and Multi-scale Pyramid in Transformer for Video-based Pedestrian Retrieval", IEEE Transactions on Industrial Informatics, 2022 (* Peking*). [Paper]
  • ?: "Motion-Aware Transformer For Occluded Person Re-identification", arXiv, 2022 (NetEase, China). [Paper]
  • PFT: "Short Range Correlation Transformer for Occluded Person Re-Identification", arXiv, 2022 (Nanjing University of Posts and Telecommunications). [Paper]

[Back to Overview]

Face

  • General:
    • FAU-Transformer: "Facial Action Unit Detection With Transformers", CVPR, 2021 (Rakuten Institute of Technology). [Paper]
    • Clusformer: "Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition", CVPR, 2021 (VinAI Research, Vietnam). [Paper][Code (in construction)]
    • Latent-Transformer: "A Latent Transformer for Disentangled Face Editing in Images and Videos", ICCV, 2021 (Institut Polytechnique de Paris). [Paper][PyTorch]
    • TADeT: "Mitigating Bias in Visual Transformers via Targeted Alignment", BMVC, 2021 (Gerogia Tech). [Paper]
    • ViT-Face: "Face Transformer for Recognition", arXiv, 2021 (Beijing University of Posts and Telecommunications). [Paper]
    • TransRPPG: "TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection", arXiv, 2021 (University of Oulu). [Paper]
    • FaceT: "Learning to Cluster Faces via Transformer", arXiv, 2021 (Alibaba). [Paper]
    • VidFace: "VidFace: A Full-Transformer Solver for Video Face Hallucination with Unaligned Tiny Snapshots", arXiv, 2021 (Zhejiang University). [Paper]
    • FAA: "Shuffle Transformer with Feature Alignment for Video Face Parsing", arXiv, 2021 (Tencent). [Paper]
    • LOTR: "LOTR: Face Landmark Localization Using Localization Transformer", arXiv, 2021 (Sertis, Thailand). [Paper]
    • FAT: "Facial Attribute Transformers for Precise and Robust Makeup Transfer", WACV, 2022 (University of Rochester). [Paper]
    • SSAT: "SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal", AAAI, 2022 (Wuhan University). [Paper][PyTorch]
    • ICT: "Protecting Celebrities with Identity Consistency Transformer", CVPR, 2022 (Microsoft). [Paper]
    • SLPT: "Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning", CVPR, 2022 (University of Technology Sydney). [Paper][PyTorch]
    • TransEditor: "TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing", CVPR, 2022 (Shanghai AI La). [Paper][PyTorch][Website]
    • RestoreFormer: "RestoreFormer: High-Quality Blind Face Restoration From Undegraded Key-Value Pairs", arXiv, 2022 (HKU). [Paper]
    • EventFormer: "EventFormer: AU Event Transformer for Facial Action Unit Event Detection", arXiv, 2022 (Peking). [Paper]
    • MFT: "Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers", arXiv, 2022 (SUNY Binghamton). [Paper]
    • VC-TRSF: "Self-supervised Video-centralised Transformer for Video Face Clustering", arXiv, 2022 (ICL). [Paper]
  • Facial Expression:
    • TransFER: "TransFER: Learning Relation-aware Facial Expression Representations with Transformers", ICCV, 2021 (CAS). [Paper]
    • CVT-Face: "Robust Facial Expression Recognition with Convolutional Visual Transformers", arXiv, 2021 (Hunan University). [Paper]
    • MViT: "MViT: Mask Vision Transformer for Facial Expression Recognition in the wild", arXiv, 2021 (University of Science and Technology of China). [Paper]
    • ViT-SE: "Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition", arXiv, 2021 (CentraleSupélec, France). [Paper]
    • EST: "Expression Snippet Transformer for Robust Video-based Facial Expression Recognition", arXiv, 2021 (China University of Geosciences). [Paper][PyTorch]
    • MFEViT: "MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition", arXiv, 2021 (University of Science and Technology of China). [Paper]
    • F-PDLS: "Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task", ICASSP, 2022 (KAIST). [Paper]
    • ?: "Transformer-based Multimodal Information Fusion for Facial Expression Analysis", arXiv, 2022 (Netease, China). [Paper]
    • ?: "Facial Expression Recognition with Swin Transformer", arXiv, 2022 (Dongguk University, Korea). [Paper]
    • POSTER: "POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition", arXiv, 2022 (UCF). [Paper]
    • STT: "Spatio-Temporal Transformer for Dynamic Facial Expression Recognition in the Wild", arXiv, 2022 (*Hunan University *). [Paper]
  • Attack-related:
    • ?: "Video Transformer for Deepfake Detection with Incremental Learning", ACMMM, 2021 (MBZUAI). [Paper]
    • ViTranZFAS: "On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing", International Joint Conference on Biometrics (IJCB), 2021 (Idiap). [Paper]
    • MTSS: "Multi-Teacher Single-Student Visual Transformer with Multi-Level Attention for Face Spoofing Detection", BMVC, 2021 (National Taiwan Ocean University). [Paper]
    • CViT: "Deepfake Video Detection Using Convolutional Vision Transformer", arXiv, 2021 (Jimma University). [Paper]
    • ViT-Distill: "Deepfake Detection Scheme Based on Vision Transformer and Distillation", arXiv, 2021 (Sookmyung Women’s University). [Paper]
    • M2TR: "M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection", arXiv, 2021 (Fudan University). [Paper]
    • Cross-ViT: "Combining EfficientNet and Vision Transformers for Video Deepfake Detection", arXiv, 2021 (University of Pisa). [Paper][PyTorch]
    • ?: "Self-supervised Transformer for Deepfake Detection", arXiv, 2022 (USTC, China). [Paper]
    • ViTransPAD: "ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection", arXiv, 2022 (University of La Rochelle, France). [Paper]
    • ViTAF: "Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing", arXiv, 2022 (Google). [Paper]

[Back to Overview]

Neural Architecture Search

  • HR-NAS: "HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers", CVPR, 2021 (HKU). [Paper][PyTorch]
  • CATE: "CATE: Computation-aware Neural Architecture Encoding with Transformers", ICML, 2021 (Michigan State University). [Paper]
  • AutoFormer: "AutoFormer: Searching Transformers for Visual Recognition", ICCV, 2021 (Microsoft). [Paper][PyTorch]
  • GLiT: "GLiT: Neural Architecture Search for Global and Local Image Transformer", ICCV, 2021 (The University of Sydney + SenseTime). [Paper]
  • BossNAS: "BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search", ICCV, 2021 (Monash University). [Paper][PyTorch]
  • ViT-ResNAS: "Searching for Efficient Multi-Stage Vision Transformers", ICCVW, 2021 (MIT). [Paper][PyTorch]
  • AutoformerV2: "Searching the Search Space of Vision Transformer", NeurIPS, 2021 (Microsoft). [Paper][PyTorch]
  • TNASP: "TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework", NeurIPS, 2021 (CAS + Kuaishou). [Paper]
  • ViTAS: "Vision Transformer Architecture Search", arXiv, 2021 (The University of Sydney + SenseTime). [Paper]
  • PSViT: "PSViT: Better Vision Transformer via Token Pooling and Attention Sharing", arXiv, 2021 (The University of Sydney + SenseTime). [Paper]
  • UniNet: "UniNet: Unified Architecture Search with Convolution, Transformer, and MLP", arXiv, 2021 (CUHK + SenseTime). [Paper]
  • As-ViT: "Auto-scaling Vision Transformers without Training", ICLR, 2022 (UT Austin). [Paper][PyTorch]
  • NASViT: "NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training", ICLR, 2022 (Facebook). [Paper]
  • TF-TAS: "Training-free Transformer Architecture Search", CVPR, 2022 (Tencent). [Paper]
  • ViT-Slim: "Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space", arXiv, 2022 (MBZUAI). [Paper]
  • VTCAS: "Vision Transformer with Convolutions Architecture Search", arXiv, 2022 (Donghua University). [Paper]

[Back to Overview]

Transfer / X-Supervised / X-Shot / Continual Learning

  • Domain Adaptation/Generalization:
    • TransDA: "Transformer-Based Source-Free Domain Adaptation", arXiv, 2021 (Haerbin Institute of Technology). [Paper][PyTorch]
    • TVT: "TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation", arXiv, 2021 (UT Arlington + Kuaishou). [Paper]
    • ResTran: "Discovering Spatial Relationships by Transformers for Domain Generalization", arXiv, 2021 (MBZUAI). [Paper]
    • WinTR: "Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation", arXiv, 2021 (Beijing Institute of Technology). [Paper]
    • CDTrans: "CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation", ICLR, 2022 (Alibaba). [Paper][PyTorch]
    • SSRT: "Safe Self-Refinement for Transformer-based Domain Adaptation", CVPR, 2022 (Stony Brook). [Paper]
    • BCAT: "Domain Adaptation via Bidirectional Cross-Attention Transformer", arXiv, 2022 (Southern University of Science and Technology). [Paper]
    • DoTNet: "Towards Unsupervised Domain Adaptation via Domain-Transformer", arXiv, 2022 (Sun Yat-Sen University). [Paper]
    • TransDA: "Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation", arXiv, 2022 (Tsinghua). [Paper][Code (in construction)]
    • FAMLP: "FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization", arXiv, 2022 (University of Science and Technology of China). [Paper]
  • X-Shot:
    • CrossTransformer: "CrossTransformers: spatially-aware few-shot transfer", NeurIPS, 2020 (DeepMind). [Paper][Tensorflow]
    • URT: "A Universal Representation Transformer Layer for Few-Shot Image Classification", ICLR, 2021 (Mila). [Paper][PyTorch]
    • TRX: "Temporal-Relational CrossTransformers for Few-Shot Action Recognition", CVPR, 2021 (University of Bristol). [Paper][PyTorch]
    • Few-shot-Transformer: "Few-Shot Transformation of Common Actions into Time and Space", arXiv, 2021 (University of Amsterdam). [Paper]
    • HyperTransformer: "HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning", arXiv, 2022 (Google). [Paper]
    • ViT-ZSL: "Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning", IMVIP, 2021 (University of Exeter, UK). [Paper]
    • TransZero: "TransZero: Attribute-guided Transformer for Zero-Shot Learning", AAAI, 2022 (Huazhong University of Science and Technology). [Paper][PyTorch]
    • SUN: "Self-Promoted Supervision for Few-Shot Transformer", arXiv, 2022 (Harbin Institute of Technology + NUS). [Paper][PyTorch]
    • HRT: "Hybrid Routing Transformer for Zero-Shot Learning", arXiv, 2022 (Xidian University). [Paper]
  • Continual Learning:
    • MEAT: "Meta-attention for ViT-backed Continual Learning", CVPR, 2022 (Zhejiang University). [Paper][Code (in construction)]
    • DyTox: "DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion", CVPR, 2022 (Sorbonne Universite, France). [Paper][PyTorch]
    • COLT: "Transformers Are Better Continual Learners", arXiv, 2022 (Hikvision). [Paper]
    • ?: "Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization", arXiv, 2022 (Ca' Foscari University, Italy). [Paper]

[Back to Overview]

Low-level Vision Tasks

Image Restoration

(e.g. super-resolution, image denoising, demosaicing, compression artifacts reduction, etc.)

  • NLRN: "Non-Local Recurrent Network for Image Restoration", NeurIPS, 2018 (UIUC). [Paper][Tensorflow]
  • RNAN: "Residual Non-local Attention Networks for Image Restoration", ICLR, 2019 (Northeastern University). [Paper][PyTorch]
  • SAN: "Second-Order Attention Network for Single Image Super-Resolution", CVPR, 2019 (Tsinghua). [Paper][PyTorch]
  • CS-NL: "Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining", CVPR, 2020 (UIUC). [Paper][PyTorch]
  • TTSR: "Learning Texture Transformer Network for Image Super-Resolution", CVPR, 2020 (Microsoft). [Paper][PyTorch]
  • HAN: "Single Image Super-Resolution via a Holistic Attention Network", ECCV, 2020 (Northeastern University). [Paper][PyTorch]
  • PANet: "Pyramid Attention Networks for Image Restoration", arXiv, 2020 (UIUC). [Paper][PyTorch]
  • IPT: "Pre-Trained Image Processing Transformer", CVPR, 2021 (Huawei). [Paper][PyTorch (in construction)]
  • NLSN: "Image Super-Resolution With Non-Local Sparse Attention", CVPR, 2021 (UIUC). [Paper]
  • SwinIR: "SwinIR: Image Restoration Using Swin Transformer", ICCVW, 2021 (ETHZ). [Paper][PyTorch]
  • ITSRN: "Implicit Transformer Network for Screen Content Image Continuous Super-Resolution", NeurIPS, 2021 (Tianjin University). [Paper][PyTorch]
  • SDNet: "SDNet: multi-branch for single image deraining using swin", arXiv, 2021 (Xinjiang University). [Paper][Code (in construction)]
  • FPAN: "Feedback Pyramid Attention Networks for Single Image Super-Resolution", arXiv, 2021 (Nanjing University of Science and Technology). [Paper]
  • ATTSF: "Attention! Stay Focus!", arXiv, 2021 (BridgeAI, Seoul). [Paper][Tensorflow]
  • ESRT: "Efficient Transformer for Single Image Super-Resolution", arXiv, 2021 (Peking University). [Paper]
  • Fusformer: "Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution", arXiv, 2021 (University of Electronic Science and Technology of China). [Paper]
  • HyLoG-ViT: "Hybrid Local-Global Transformer for Image Dehazing", arXiv, 2021 (Beihang University). [Paper]
  • TANet: "TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network", arXiv, 2021 (Wuhan Institute of Technology). [Paper]
  • DPT: "Detail-Preserving Transformer for Light Field Image Super-Resolution", AAAI, 2022 (Beijing Institute of Technology). [Paper][PyTorch]
  • SiamTrans: "SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers", AAAI, 2022 (Huawei). [Paper]
  • Uformer: "Uformer: A General U-Shaped Transformer for Image Restoration", CVPR, 2022 (University of Science and Technology of China). [Paper][PyTorch]
  • MAXIM: "MAXIM: Multi-Axis MLP for Image Processing", CVPR, 2022 (Google). [Paper][Tensorflow]
  • HyperTransformer: "HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening", CVPR, 2022 (JHU). [Paper][PyTorch]
  • BSRT: "BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment", CVPRW, 2022 (MEGVII). [Paper][PyTorch]
  • LBNet: "Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer", IJCAI, 2022 (Nanjing University of Posts and Telecommunications). [Paper][PyTorch (in construction)]
  • LFT: "Light Field Image Super-Resolution with Transformers", IEEE Signal Processing Letters, 2022 (National University of Defense Technology, China). [Paper][PyTorch]
  • ELAN: "Efficient Long-Range Attention Network for Image Super-resolution", arXiv, 2022 (The Hong Kong Polytechnic University). [Paper][Code (in construction)]
  • ACT: "Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution", arXiv, 2022 (LG). [Paper]
  • ?: "Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild", arXiv, 2022 (ETHZ). [Paper]
  • HIPA: "HIPA: Hierarchical Patch Transformer for Single Image Super Resolution", arXiv, 2022 (CUHK). [Paper]
  • DehazeFormer: "Vision Transformers for Single Image Dehazing", arXiv, 2022 (Zhejiang University). [Paper][PyTorch]
  • Stripformer: "Stripformer: Strip Transformer for Fast Image Deblurring", arXiv, 2022 (NTHU). [Paper]
  • RSTCANet: "Residual Swin Transformer Channel Attention Network for Image Demosaicing", arXiv, 2022 (Tampere University, Finland). [Paper]
  • CTCNet: "CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution", arXiv, 2022 (Nanjing University of Posts and Telecommunications). [Paper]
  • DRT: "DRT: A Lightweight Single Image Deraining Recursive Transformer", arXiv, 2022 (ANU, Australia). [Paper][PyTorch (in construction)]
  • HAT: "Activating More Pixels in Image Super-Resolution Transformer", arXiv, 2022 (University of Macau). [Paper][Code (in construction)]

[Back to Overview]

Video Restoration

  • VSR-Transformer: "Video Super-Resolution Transformer", arXiv, 2021 (ETHZ). [Paper][PyTorch]
  • MANA: "Memory-Augmented Non-Local Attention for Video Super-Resolution", arXiv, 2021 (JD). [Paper]
  • ?: "Bringing Old Films Back to Life", CVPR, 2022 (Microsoft). [Paper][Code (in construction)]
  • TTVSR: "Learning Trajectory-Aware Transformer for Video Super-Resolution", CVPR, 2022 (Microsoft). [Paper][PyTorch]
  • Trans-SVSR: "A New Dataset and Transformer for Stereoscopic Video Super-Resolution", CVPR, 2022 (Bahcesehir University, Turkey). [Paper][PyTorch]
  • VRT: "VRT: A Video Restoration Transformer", arXiv, 2022 (ETHZ). [Paper][PyTorch]
  • FGST: "Flow-Guided Sparse Transformer for Video Deblurring", arXiv, 2022 (Tsinghua). [Paper]
  • STDAN: "STDAN: Deformable Attention Network for Space-Time Video Super-Resolution", arXiv, 2022 (Tsinghua). [Paper]
  • RSTT: "RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution", arXiv, 2022 (Microsoft). [Paper][PyTorch]
  • VDTR: "VDTR: Video Deblurring with Transformer", arXiv, 2022 (Tsinghua). [Paper][Code (in construction)]
  • DSCT: "Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer", arXiv, 2022 (*Beijing University of Posts and Telecommunications *). [Paper]

[Back to Overview]

Inpainting / Completion / Outpainting

  • Contexual-Attention: "Generative Image Inpainting with Contextual Attention", CVPR, 2018 (UIUC). [Paper][Tensorflow]
  • PEN-Net: "Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting", CVPR, 2019 (Microsoft). [Paper][PyTorch]
  • Copy-Paste: "Copy-and-Paste Networks for Deep Video Inpainting", ICCV, 2019 (Yonsei University). [Paper][PyTorch]
  • Onion-Peel: "Onion-Peel Networks for Deep Video Completion", ICCV, 2019 (Yonsei University). [Paper][PyTorch]
  • STTN: "Learning Joint Spatial-Temporal Transformations for Video Inpainting", ECCV, 2020 (Microsoft). [Paper][PyTorch]
  • FuseFormer: "FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting", ICCV, 2021 (CUHK + SenseTime). [Paper][PyTorch]
  • ICT: "High-Fidelity Pluralistic Image Completion with Transformers", ICCV, 2021 (CUHK). [Paper][PyTorch][Website]
  • DSTT: "Decoupled Spatial-Temporal Transformer for Video Inpainting", arXiv, 2021 (CUHK + SenseTime). [Paper][Code (in construction)]
  • TFill: "TFill: Image Completion via a Transformer-Based Architecture", arXiv, 2021 (NTU Singapore). [Paper][Code (in construction)]
  • BAT-Fill: "Diverse Image Inpainting with Bidirectional and Autoregressive Transformers", arXiv, 2021 (NTU Singapore). [Paper]
  • ?: "Image-Adaptive Hint Generation via Vision Transformer for Outpainting", WACV, 2022 (Sogang University, Korea). [Paper]
  • ZITS: "Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding", CVPR, 2022 (Fudan). [Paper][Code (in construction)]
  • MAT: "MAT: Mask-Aware Transformer for Large Hole Image Inpainting", CVPR, 2022 (CUHK). [Paper][PyTorch]
  • PUT: "Reduce Information Loss in Transformers for Pluralistic Image Inpainting", CVPR, 2022 (Microsoft). [Paper]
  • U-Transformer: "Generalised Image Outpainting with U-Transformer", arXiv, 2022 (Xi'an Jiaotong-Liverpool University). [Paper]

[Back to Overview]

Image Generation

  • IT: "Image Transformer", ICML, 2018 (Google). [Paper][Tensorflow]
  • PixelSNAIL: "PixelSNAIL: An Improved Autoregressive Generative Model", ICML, 2018 (Berkeley). [Paper][Tensorflow]
  • BigGAN: "Large Scale GAN Training for High Fidelity Natural Image Synthesis", ICLR, 2019 (DeepMind). [Paper][PyTorch]
  • SAGAN: "Self-Attention Generative Adversarial Networks", ICML, 2019 (Google). [Paper][Tensorflow]
  • VQGAN: "Taming Transformers for High-Resolution Image Synthesis", CVPR, 2021 (Heidelberg University). [Paper][PyTorch][Website]
  • ?: "High-Resolution Complex Scene Synthesis with Transformers", CVPRW, 2021 (Heidelberg University). [Paper]
  • GANsformer: "Generative Adversarial Transformers", ICML, 2021 (Stanford + Facebook). [Paper][Tensorflow]
  • PixelTransformer: "PixelTransformer: Sample Conditioned Signal Generation", ICML, 2021 (Facebook). [Paper][Website]
  • HWT: "Handwriting Transformers", ICCV, 2021 (MBZUAI). [Paper][Code (in construction)]
  • Paint-Transformer: "Paint Transformer: Feed Forward Neural Painting with Stroke Prediction", ICCV, 2021 (Baidu). [Paper][Paddle][PyTorch]
  • Geometry-Free: "Geometry-Free View Synthesis: Transformers and no 3D Priors", ICCV, 2021 (Heidelberg University). [Paper][PyTorch]
  • VTGAN: "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers", ICCVW, 2021 (University of Nevada, Reno). [Paper]
  • ATISS: "ATISS: Autoregressive Transformers for Indoor Scene Synthesis", NeurIPS, 2021 (NVIDIA). [Paper][Website]
  • GANsformer2: "Compositional Transformers for Scene Generation", NeurIPS, 2021 (Stanford + Facebook). [Paper][Tensorflow]
  • TransGAN: "TransGAN: Two Transformers Can Make One Strong GAN", NeurIPS, 2021 (UT Austin). [Paper][PyTorch]
  • HiT: "Improved Transformer for High-Resolution GANs", NeurIPS, 2021 (Google). [Paper][Tensorflow]
  • iLAT: "The Image Local Autoregressive Transformer", NeurIPS, 2021 (Fudan). [Paper]
  • TokenGAN: "Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers", NeurIPS, 2021 (Microsoft). [Paper]
  • SceneFormer: "SceneFormer: Indoor Scene Generation with Transformers", arXiv, 2021 (TUM). [Paper]
  • SNGAN: "Combining Transformer Generators with Convolutional Discriminators", arXiv, 2021 (Fraunhofer ITWM). [Paper]
  • Styleformer: "Styleformer: Transformer based Generative Adversarial Networks with Style Vector", arXiv, 2021 (Seoul National University). [Paper][PyTorch]
  • Invertible-Attention: "Invertible Attention", arXiv, 2021 (ANU). [Paper]
  • GPA: "Grid Partitioned Attention: Efficient Transformer Approximation with Inductive Bias for High Resolution Detail Generation", arXiv, 2021 (Zalando Research, Germany). [Paper][PyTorch (in construction)]
  • StyleSwin: "StyleSwin: Transformer-based GAN for High-resolution Image Generation", arXiv, 2021 (Microsoft). [Paper]
  • ViTGAN: "ViTGAN: Training GANs with Vision Transformers", ICLR, 2022 (Google). [Paper][PyTorch (wilile26811249)]
  • ViT-VQGAN: "Vector-quantized Image Modeling with Improved VQGAN", ICLR, 2022 (Google). [Paper]
  • Style-Transformer: "Style Transformer for Image Inversion and Editing", CVPR, 2022 (East China Normal University). [Paper][Code (in construction)]
  • U-Attention: "Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis", arXiv, 2022 (Adobe). [Paper]
  • MaskGIT: "MaskGIT: Masked Generative Image Transformer", arXiv, 2022 (Google). [Paper][PyTorch (dome272)]
  • ViT-Patch: "A Robust Framework of Chromosome Straightening with ViT-Patch GAN", arXiv, 2022 (Xi'an Jiaotong-Liverpool University). [Paper]
  • ViewFormer: "ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers", arXiv, 2022 (Czech Technical University in Prague). [Paper][Tensorflow]

[Back to Overview]

Video Generation

  • Subscale: "Scaling Autoregressive Video Models", ICLR, 2020 (Google). [Paper][Website]
  • ConvTransformer: "ConvTransformer: A Convolutional Transformer Network for Video Frame Synthesis", arXiv, 2020 (Southeast University). [Paper]
  • OCVT: "Generative Video Transformer: Can Objects be the Words?", ICML, 2021 (Rutgers University). [Paper]
  • AIST++: "Learn to Dance with AIST++: Music Conditioned 3D Dance Generation", arXiv, 2021 (Google). [Paper][Code][Website]
  • VideoGPT: "VideoGPT: Video Generation using VQ-VAE and Transformers", arXiv, 2021 (Berkeley). [Paper][PyTorch][Website]
  • DanceFormer: "DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer", AAAI, 2022 (Huiye Technology, China). [Paper]
  • Transframer: "Transframer: Arbitrary Frame Prediction with Generative Models", arXiv, 2022 (DeepMind). [Paper]
  • TATS: "Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer", arXiv, 2022 (Maryland). [Paper][Website]

[Back to Overview]

Transfer / Translation / Manipulation

  • AdaAttN: "AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer", ICCV, 2021 (Baidu). [Paper][Paddle][PyTorch]
  • StyTr2: "StyTr^2: Unbiased Image Style Transfer with Transformers", arXiv, 2021 (CAS). [Paper]
  • InstaFormer: "InstaFormer: Instance-Aware Image-to-Image Translation with Transformer", CVPR, 2022 (Korea University). [Paper]
  • ManiTrans: "ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation", CVPR, 2022 (Huawei). [Paper][Website]
  • Splice: "Splicing ViT Features for Semantic Appearance Transfer", arXiv, 2022 (Weizmann Institute of Science, Israel). [Paper][PyTorch][Website]
  • UVCGAN: "UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation", arXiv, 2022 (Brookhaven National Laboratory, NY). [Paper]
  • ITTR: "ITTR: Unpaired Image-to-Image Translation with Transformers", arXiv, 2022 (Kuaishou). [Paper]

[Back to Overview]

Other Low-Level Tasks

  • Colorization:
    • ColTran: "Colorization Transformer", ICLR, 2021 (Google). [Paper][Tensorflow]
    • ViT-I-GAN: "ViT-Inception-GAN for Image Colourising", arXiv, 2021 (D.Y Patil College of Engineering, India). [Paper]
  • Enhancement:
    • STAR: "STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement", ICCV, 2021 (CUHK + SenseBrain). [Paper]
    • PanFormer: "PanFormer: a Transformer Based Model for Pan-sharpening", ICME, 2022 (Beihang University). [Paper][PyTorch]
    • URSCT-UIE: "Reinforced Swin-Convs Transformer for Underwater Image Enhancement", arXiv, 2022 (Ningbo University). [Paper]
  • Harmonization:
    • HT: "Image Harmonization With Transformer", ICCV, 2021 (Ocean University of China). [Paper]
  • Image Compression:
    • ?: "Towards End-to-End Image Compression and Analysis with Transformers", AAAI, 2022 (1Harbin Institute of Technology). [Paper][PyTorch]
    • Entroformer: "Entroformer: A Transformer-based Entropy Model for Learned Image Compression", ICLR, 2022 (Alibaba). [Paper]
    • Contextformer: "Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression", arXiv, 2022 (TUM). [Paper]
  • Matting:
    • MatteFormer: "MatteFormer: Transformer-Based Image Matting via Prior-Tokens", arXiv, 2022 (SNU + NAVER). [Paper]
  • Reconstruction
    • ET-Net: "Event-Based Video Reconstruction Using Transformer", ICCV, 2021 (University of Science and Technology of China). [Paper][PyTorch]
    • GradViT: "GradViT: Gradient Inversion of Vision Transformers", CVPR, 2022 (NVIDIA). [Paper][Website]
    • MST: "Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction", CVPR, 2022 (Tsinghua). [Paper][Code (in construction)]
    • MST++: "MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction", CVPRW, 2022 (Tsinghua). [Paper][PyTorch]
  • Others:
    • MS-Unet: "Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer", arXiv, 2021 (MEGVII). [Paper]
    • TransMEF: "TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning", AAAI, 2022 (Fudan). [Paper]
    • GAP-CSCoT: "Spectral Compressive Imaging Reconstruction Using Convolution and Spectral Contextual Transformer", arXiv, 2022 (CAS). [Paper]
    • CST: "Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction", arXiv, 2022 (Tsinghua). [Paper]

[Back to Overview]

Reinforcement Learning

Navigation

  • VTNet: "VTNet: Visual Transformer Network for Object Goal Navigation", ICLR, 2021 (ANU). [Paper]
  • MaAST: "MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation", ICRA, 2021 (SRI). [Paper]
  • TransFuser: "Multi-Modal Fusion Transformer for End-to-End Autonomous Driving", CVPR, 2021 (MPI). [Paper][PyTorch]
  • CMTP: "Topological Planning With Transformers for Vision-and-Language Navigation", CVPR, 2021 (Stanford). [Paper]
  • VLN-BERT: "VLN-BERT: A Recurrent Vision-and-Language BERT for Navigation", CVPR, 2021 (ANU). [Paper][PyTorch]
  • E.T.: "Episodic Transformer for Vision-and-Language Navigation", ICCV, 2021 (Google). [Paper][PyTorch]
  • HAMT: "History Aware Multimodal Transformer for Vision-and-Language Navigation", NeurIPS, 2021 (INRIA). [Paper][PyTorch][Website]
  • SOAT: "SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation", NeurIPS, 2021 (Georgia Tech). [Paper]
  • OMT: "Object Memory Transformer for Object Goal Navigation", ICRA, 2022 (AIST, Japan). [Paper]
  • DUET: "Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation", arXiv, 2022 (INRIA). [Paper][Website]

[Back to Overview]

Other RL Tasks

  • SVEA: "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation", arXiv, 2021 (UCSD). [Paper][GitHub][Website]
  • LocoTransformer: "Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers", ICLR, 2022 (UCSD). [Paper][Website]
  • STAM: "Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes", CVPR, 2022 (McGill University, Canada). [Paper][PyTorch]
  • RAD: "Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels", arXiv, 2022 (UBC, Canada). [Paper]

[Back to Overview]

Medical

Medical Segmentation

  • Cross-Transformer: "The entire network structure of Crossmodal Transformer", ICBSIP, 2021 (Capital Medical University). [Paper]
  • Segtran: "Medical Image Segmentation using Squeeze-and-Expansion Transformers", IJCAI, 2021 (A*STAR). [Paper]
  • i-ViT: "Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image", MICCAI, 2021 (Xi'an Jiaotong University). [Paper][PyTorch][Website]
  • UTNet: "UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation", MICCAI, 2021 (Rutgers). [Paper]
  • MCTrans: "Multi-Compound Transformer for Accurate Biomedical Image Segmentation", MICCAI, 2021 (HKU + CUHK). [Paper][Code (in construction)]
  • Polyformer: "Few-Shot Domain Adaptation with Polymorphic Transformers", MICCAI, 2021 (A*STAR). [Paper][PyTorch]
  • BA-Transformer: "Boundary-aware Transformers for Skin Lesion Segmentation". MICCAI, 2021 (Xiamen University). [Paper][PyTorch]
  • GT-U-Net: "GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation", MICCAIW, 2021 (Hangzhou Dianzi University). [Paper][PyTorch]
  • STN: "Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation", ISBI, 2021 (Institut Polytechnique de Paris). [Paper]
  • T-AutoML: "T-AutoML: Automated Machine Learning for Lesion Segmentation Using Transformers in 3D Medical Imaging", ICCV, 2021 (NVIDIA). [Paper]
  • MedT: "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation", arXiv, 2021 (Johns Hopkins). [Paper][PyTorch]
  • Convolution-Free: "Convolution-Free Medical Image Segmentation using Transformers", arXiv, 2021 (Harvard). [Paper]
  • CoTR: "CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation", arXiv, 2021 (Northwestern Polytechnical University). [Paper][PyTorch]
  • TransBTS: "TransBTS: Multimodal Brain Tumor Segmentation Using Transformer", arXiv, 2021 (University of Science and Technology Beijing). [Paper][PyTorch]
  • SpecTr: "SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation", arXiv, 2021 (East China Normal University). [Paper][Code (in construction)]
  • U-Transformer: "U-Net Transformer: Self and Cross Attention for Medical Image Segmentation", arXiv, 2021 (CEDRIC). [Paper]
  • TransUNet: "TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation", arXiv, 2021 (Johns Hopkins). [Paper][PyTorch]
  • PMTrans: "Pyramid Medical Transformer for Medical Image Segmentation", arXiv, 2021 (Washington University in St. Louis). [Paper]
  • PBT-Net: "Anatomy-Guided Parallel Bottleneck Transformer Network for Automated Evaluation of Root Canal Therapy", arXiv, 2021 (Hangzhou Dianzi University). [Paper]
  • Swin-Unet: "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation", arXiv, 2021 (Huawei). [Paper][Code (in construction)]
  • MBT-Net: "A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation", arXiv, 2021 (Southern University of Science and Technology). [Paper]
  • WAD: "More than Encoder: Introducing Transformer Decoder to Upsample", arXiv, 2021 (South China University of Technology). [Paper]
  • LeViT-UNet: "LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation", arXiv, 2021 (Wuhan Institute of Technology). [Paper]
  • Polyp-PVT: "Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers", arXiv, 2021 (IIAI). [Paper][PyTorch]
  • ?: "Evaluating Transformer based Semantic Segmentation Networks for Pathological Image Segmentation", arXiv, 2021 (Vanderbilt University). [Paper]
  • nnFormer: "nnFormer: Interleaved Transformer for Volumetric Segmentation", arXiv, 2021 (HKU + Xiamen University). [Paper][PyTorch]
  • MISSFormer: "MISSFormer: An Effective Medical Image Segmentation Transformer", arXiv, 2021 (Beijing University of Posts and Telecommunications). [Paper]
  • TUnet: "Transformer-Unet: Raw Image Processing with Unet", arXiv, 2021 (Beijing Zoezen Robot + Beihang University). [Paper]
  • BiTr-Unet: "BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation", arXiv, 2021 (New York University). [Paper]
  • ?: "Transformer Assisted Convolutional Network for Cell Instance Segmentation", arXiv, 2021 (IIT Dhanbad). [Paper]
  • ?: "Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining", arXiv, 2021 (Ukrainian Catholic University). [Paper]
  • UNETR: "UNETR: Transformers for 3D Medical Image Segmentation", WACV, 2022 (NVIDIA). [Paper][PyTorch]
  • AFTer-UNet: "AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation", WACV, 2022 (UC Irvine). [Paper]
  • UCTransNet: "UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer", AAAI, 2022 (Northeastern University, China). [Paper][PyTorch]
  • Tempera: "Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation", arXiv, 2022 (ICL). [Paper]
  • UTNetV2: "A Multi-scale Transformer for Medical Image Segmentation: Architectures, Model Efficiency, and Benchmarks", arXiv, 2022 (Rutgers). [Paper]
  • UNesT: "Characterizing Renal Structures with 3D Block Aggregate Transformers", arXiv, 2022 (Vanderbilt University, Tennessee). [Paper]
  • PHTrans: "PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation", arXiv, 2022 (Beijing University of Posts and Telecommunications). [Paper]
  • UNeXt: "UNeXt: MLP-based Rapid Medical Image Segmentation Network", arXiv, 2022 (JHU). [Paper][PyTorch]
  • TransFusion: "TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers", arXiv, 2022 (Rutgers). [Paper]
  • UNetFormer: "UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation", arXiv, 2022 (NVIDIA). [Paper][GitHub]
  • 3D-Shuffle-Mixer: "3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume", arXiv, 2022 (Xi'an Jiaotong University). [Paper]
  • ?: "Continual Hippocampus Segmentation with Transformers", arXiv, 2022 (Technical University of Darmstadt, Germany). [Paper]
  • TranSiam: "TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation", arXiv, 2022 (Tianjin University). [Paper]

[Back to Overview]

Medical Classification

  • COVID19T: "A Transformer-Based Framework for Automatic COVID19 Diagnosis in Chest CTs", ICCVW, 2021 (?). [Paper][PyTorch]
  • TransMIL: "TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classication", NeurIPS, 2021 (Tsinghua University). [Paper][PyTorch]
  • TransMed: "TransMed: Transformers Advance Multi-modal Medical Image Classification", arXiv, 2021 (Northeastern University). [Paper]
  • CXR-ViT: "Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification", arXiv, 2021 (KAIST). [Paper]
  • ViT-TSA: "Shoulder Implant X-Ray Manufacturer Classification: Exploring with Vision Transformer", arXiv, 2021 (Queen’s University). [Paper]
  • GasHis-Transformer: "GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathology Image Classification", arXiv, 2021 (Northeastern University). [Paper]
  • POCFormer: "POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound", arXiv, 2021 (The Ohio State University). [Paper]
  • COVID-ViT: "COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models", arXiv, 2021 (Middlesex University, UK). [Paper][PyTorch]
  • EEG-ConvTransformer: "EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification", arXiv, 2021 (IIT Ropar). [Paper]
  • CCAT: "Visual Transformer with Statistical Test for COVID-19 Classification", arXiv, 2021 (NCKU). [Paper]
  • ScoreNet: "ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification", arXiv, 2022 (EPFL). [Paper]
  • RadioTransformer: "RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification", arXiv, 2022 (Stony Brook). [Paper]
  • LA-MIL: "Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction", arXiv, 2022 (TUM). [Paper]

[Back to Overview]

Medical Detection

  • COTR: "COTR: Convolution in Transformer Network for End to End Polyp Detection", arXiv, 2021 (Fuzhou University). [Paper]
  • TR-Net: "Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries", arXiv, 2021 (Harbin Institute of Technology). [Paper]
  • CAE-Transformer: "CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans", arXiv, 2021 (Concordia University, Canada). [Paper]
  • DATR: "DATR: Domain-adaptive transformer for multi-domain landmark detection", arXiv, 2022 (CAS). [Paper]
  • SATr: "SATr: Slice Attention with Transformer for Universal Lesion Detection", arXiv, 2022 (CAS). [Paper]

[Back to Overview]

Medical Reconstruction

  • T2Net: "Task Transformer Network for Joint MRI Reconstruction and Super-Resolution", MICCAI, 2021 (Harbin Institute of Technology). [Paper][PyTorch]
  • FIT: "Fourier Image Transformer", arXiv, 2021 (MPI). [Paper][PyTorch]
  • SLATER: "Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers", arXiv, 2021 (Bilkent University). [Paper]
  • MTrans: "MTrans: Multi-Modal Transformer for Accelerated MR Imaging", arXiv, 2021 (Harbin Institute of Technology). [Paper][PyTorch]
  • ?: "Adaptively Re-weighting Multi-Loss Untrained Transformer for Sparse-View Cone-Beam CT Reconstruction", arXiv, 2022 (Zhejiang Lab). [Paper]

[Back to Overview]

Medical Low-Level Vision

  • Eformer: "Eformer: Edge Enhancement based Transformer for Medical Image Denoising", ICCV, 2021 (BITS Pilani, India). [Paper]
  • PTNet: "PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer", arXiv, 2021 (* Columbia *). [Paper]
  • ResViT: "ResViT: Residual vision transformers for multi-modal medical image synthesis", arXiv, 2021 (Bilkent University, Turkey). [Paper]
  • CyTran: "CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation", arXiv, 2021 (University Politehnica of Bucharest, Romania). [Paper][PyTorch]
  • McMRSR: "Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution", CVPR, 2022 (Yantai University, China). [Paper][PyTorch]
  • RFormer: "RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark", arXiv, 2022 (Tsinghua). [Paper]
  • CTformer: "CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising", arXiv, 2022 (UMass Lowell). [Paper][PyTorch]
  • Cohf-T: "Cross-Modality High-Frequency Transformer for MR Image Super-Resolution", arXiv, 2022 (Xidian University). [Paper]
  • SIST: "Low-Dose CT Denoising via Sinogram Inner-Structure Transformer", arXiv, 2022 (?). [Paper]

[Back to Overview]

Medical Others

  • LAT: "Lesion-Aware Transformers for Diabetic Retinopathy Grading", CVPR, 2021 (USTC). [Paper]
  • UVT: "Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation", MICCAI, 2021 (ICL). [Paper][PyTorch]
  • ?: "Surgical Instruction Generation with Transformers", MICCAI, 2021 (Bournemouth University, UK). [Paper]
  • AlignTransformer: "AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation", MICCAI, 2021 (Peking University). [Paper]
  • MCAT: "Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images", ICCV, 2021 (Harvard). [Paper][PyTorch]
  • ?: "Is it Time to Replace CNNs with Transformers for Medical Images?", ICCVW, 2021 (KTH, Sweden). [Paper]
  • HAT-Net: "HAT-Net: A Hierarchical Transformer Graph Neural Network for Grading of Colorectal Cancer Histology Images", BMVC, 2021 (Beijing University of Posts and Telecommunications). [Paper]
  • ?: "Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training", NeurIPS, 2021 (KAIST). [Paper]
  • ViT-Path: "Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology", NeurIPSW, 2021 (Microsoft). [Paper]
  • Global-Local-Transformer: "Global-Local Transformer for Brain Age Estimation", IEEE Transactions on Medical Imaging, 2021 (Harvard). [Paper][PyTorch]
  • CE-TFE: "Deep Transformers for Fast Small Intestine Grounding in Capsule Endoscope Video", arXiv, 2021 (Sun Yat-Sen University). [Paper]
  • DeepProg: "DeepProg: A Transformer-based Framework for Predicting Disease Prognosis", arXiv, 2021 (University of Oulu). [Paper]
  • ViT-V-Net: "ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration", arXiv, 2021 (JHU). [Paper][PyTorch]
  • Medical-Transformer: "Medical Transformer: Universal Brain Encoder for 3D MRI Analysis", arXiv, 2021 (Korea University). [Paper]
  • RATCHET: "RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting", arXiv, 2021 (ICL). [Paper]
  • C2FViT: "Affine Medical Image Registration with Coarse-to-Fine Vision Transformer", CVPR, 2022 (HKUST). [Paper][Code (in construction)]
  • CSM: "Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection", arXiv, 2022 (University of Adelaide, Australia). [Paper]
  • SiT: "Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis", Medical Imaging with Deep Learning (MIDL), 2022 (King’s College London, UK). [Paper]
  • SiT: "Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces", arXiv, 2022 (King’s College London, UK). [Paper][PyTorch (in construction)]
  • MDBERT: "Hierarchical BERT for Medical Document Understanding", arXiv, 2022 (IQVIA, NC). [Paper]
  • SymTrans: "Symmetric Transformer-based Network for Unsupervised Image Registration", arXiv, 2022 (Jilin University). [Paper]
  • MMT: "One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation", arXiv, 2022 (JHU). [Paper]

[Back to Overview]

Other Tasks

  • Active Learning:
    • TJLS: "Visual Transformer for Task-aware Active Learning", arXiv, 2021 (ICL). [Paper][PyTorch]
  • Animation-related:
    • AnT: "The Animation Transformer: Visual Correspondence via Segment Matching", ICCV, 2021 (Cadmium). [Paper]
    • AniFormer: "AniFormer: Data-driven 3D Animation with Transformer", BMVC, 2021 (University of Oulu, Finland). [Paper][PyTorch]
  • Biology:
    • ?: "A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: from Traditional Image Processing and Classical Machine Learning to Current Deep Convolutional Neural Networks and Potential Visual Transformers", arXiv, 2021 (Northeastern University). [Paper]
  • Camera-related:
    • CTRL-C: "CTRL-C: Camera calibration TRansformer with Line-Classification", ICCV, 2021 (Kakao + Kookmin University). [Paper][PyTorch]
    • MS-Transformer: "Learning Multi-Scene Absolute Pose Regression with Transformers", ICCV, 2021 (Bar-Ilan University, Israel). [Paper][PyTorch]
  • Character Recognition:
    • BTTR: "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer", arXiv, 2021 (Peking). [Paper]
    • TrOCR: "TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models", arXiv, 2021 (Microsoft). [Paper][PyTorch]
    • ?: "Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks", arXiv, 2021 (Salesforce). [Paper]
    • T3: "TrueType Transformer: Character and Font Style Recognition in Outline Format", Document Analysis Systems (DAS), 2022 (Kyushu University). [Paper]
    • ?: "Transformer-based HTR for Historical Documents", ComHum, 2022 (University of Zurich, Switzerland). [Paper]
  • Crowd Counting:
    • CC-AV: "Audio-Visual Transformer Based Crowd Counting", ICCVW, 2021 (University of Kansas). [Paper]
    • TransCrowd: "TransCrowd: Weakly-Supervised Crowd Counting with Transformer", arXiv, 2021 (Huazhong University of Science and Technology). [Paper][PyTorch]
    • TAM-RTM: "Boosting Crowd Counting with Transformers", arXiv, 2021 (ETHZ). [Paper]
    • CCTrans: "CCTrans: Simplifying and Improving Crowd Counting with Transformer", arXiv, 2021 (Meituan). [Paper]
    • SAANet: "Scene-Adaptive Attention Network for Crowd Counting", arXiv, 2022 (Xi'an Jiaotong). [Paper]
    • JCTNet: "Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting", arXiv, 2022 (Chongqing University). [Paper]
    • CrowdMLP: "CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP", arXiv, 2022 (University of Guelph, Canada). [Paper]
  • Curriculum Learning:
    • SSTN: "Spatial Transformer Networks for Curriculum Learning", arXiv, 2021 (TU Kaiserslautern, Germany). [Paper]
  • Digital Holography:
    • ?: "Convolutional Neural Network (CNN) vs Visual Transformer (ViT) for Digital Holography", ICCCR, 2022 (UBFC, France). [Paper]
  • Event data:
    • EvT: "Event Transformer: A sparse-aware solution for efficient event data processing", arXiv, 2022 (Universidad de Zaragoza, Spain). [Paper][PyTorch]
    • ETB: "Event Transformer", arXiv, 2022 (Nanjing University). [Paper]
  • Fashion:
    • Kaleido-BERT: "Kaleido-BERT: Vision-Language Pre-training on Fashion Domain", CVPR, 2021 (Alibaba). [Paper][Tensorflow]
    • CIT: "Cloth Interactive Transformer for Virtual Try-On", arXiv, 2021 (University of Trento). [Paper][Code (in construction)]
    • ClothFormer: "ClothFormer: Taming Video Virtual Try-on in All Module", CVPR, 2022 (iQIYI). [Paper][Website]
    • OutfitTransformer: "OutfitTransformer: Learning Outfit Representations for Fashion Recommendation", arXiv, 2022 (Amazon). [Paper]
    • Fashionformer: "Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition", arXiv, 2022 (Peking). [Paper][Code (in construction)]
  • Feature Matching:
    • SuperGlue: "SuperGlue: Learning Feature Matching with Graph Neural Networks", CVPR, 2020 (Magic Leap). [Paper][PyTorch]
    • LoFTR: "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR, 2021 (Zhejiang University). [Paper][PyTorch][Website]
    • COTR: "COTR: Correspondence Transformer for Matching Across Images", ICCV, 2021 (UBC). [Paper]
    • CATs: "CATs: Cost Aggregation Transformers for Visual Correspondence", NeurIPS, 2021 (Yonsei University + Korea University). [Paper][PyTorch][Website]
    • TransforMatcher: "TransforMatcher: Match-to-Match Attention for Semantic Correspondence", CVPR, 2022 (POSTECH). [Paper]
    • CATs++: "CATs++: Boosting Cost Aggregation with Convolutions and Transformers", arXiv, 2022 (Korea University). [Paper]
    • LoFTR-TensorRT: "Local Feature Matching with Transformers for low-end devices", arXiv, 2022 (?). [Paper][PyTorch]
    • MatchFormer: "MatchFormer: Interleaving Attention in Transformers for Feature Matching", arXiv, 2022 (Karlsruhe Institute of Technology, Germany). [Paper]
    • OpenGlue: "OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching", arXiv, 2022 (Ukrainian Catholic University). [Paper][PyTorch]
  • Fine-grained:
    • ViT-FGVC: "Exploring Vision Transformers for Fine-grained Classification", CVPRW, 2021 (Universidad de Valladolid). [Paper]
    • FFVT: "Feature Fusion Vision Transformer for Fine-Grained Visual Categorization", BMVC, 2021 (Griffith University, Australia). [Paper][PyTorch]
    • TPSKG: "Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition", arXiv, 2021 (Beihang University). [Paper]
    • AFTrans: "A free lunch from ViT: Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition", arXiv, 2021 (Peking University). [Paper]
    • TransFG: "TransFG: A Transformer Architecture for Fine-grained Recognition", AAAI, 2022 (Johns Hopkins). [Paper][PyTorch]
    • DynamicMLP: "Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information", CVPR, 2022 (Megvii). [Paper][PyTorch]
    • MetaFormer: "MetaFormer: A Unified Meta Framework for Fine-Grained Recognition", arXiv, 2022 (ByteDance). [Paper][PyTorch]
    • ViT-FOD: "ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator", arXiv, 2022 (Shandong University). [Paper]
  • Gait:
    • Gait-TR: "Spatial Transformer Network on Skeleton-based Gait Recognition", arXiv, 2022 (South China University of Technology). [Paper]
  • Gaze:
    • GazeTR: "Gaze Estimation using Transformer", arXiv, 2021 (Beihang University). [Paper][PyTorch]
    • HGTTR: "End-to-End Human-Gaze-Target Detection with Transformers", arXiv, 2022 (Shanghai Jiao Tong). [Paper]
  • Geo-Localization:
    • EgoTR: "Cross-view Geo-localization with Evolving Transformer", arXiv, 2021 (Shenzhen University). [Paper]
    • TransGeo: "TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization", CVPR, 2022 (UCF). [Paper][Code (in construction)]
    • TransGCNN: "Transformer-Guided Convolutional Neural Network for Cross-View Geolocalization", arXiv, 2022 (Southeast University, China). [Paper]
    • TransLocator: "Where in the World is this Image? Transformer-based Geo-localization in the Wild", arXiv, 2022 (JHU). [Paper]
    • MGTL: "Mutual Generative Transformer Learning for Cross-view Geo-localization", arXiv, 2022 (University of Electronic Science and Technology of China). [Paper]
  • Homography Estimation:
    • LocalTrans: "LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation", ICCV, 2021 (Tsinghua). [Paper]
  • Image Quality Assessment:
    • TRIQ: "Transformer for Image Quality Assessment", arXiv, 2020 (NORCE). [Paper][Tensorflow-Keras]
    • IQT: "Perceptual Image Quality Assessment with Transformers", CVPRW, 2021 (LG). [Paper][Code (in construction)]
    • MUSIQ: "MUSIQ: Multi-scale Image Quality Transformer", ICCV, 2021 (Google). [Paper]
    • TranSLA: "Saliency-Guided Transformer Network Combined With Local Embedding for No-Reference Image Quality Assessment", ICCVW, 2021 (Hikvision). [Paper]
    • TReS: "No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency", WACV, 2022 (CMU). [Paper]
    • IQA-Conformer: "Conformer and Blind Noisy Students for Improved Image Quality Assessment", CVPRW, 2022 (University of Wurzburg, Germany). [Paper][PyTorch]
    • SwinIQA: "SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment", CVPRW, 2022 (USTC, China). [Paper]
    • MCAS-IQA: "Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment", arXiv, 2022 (Norwegian Research Centre, Norway). [Paper]
  • Image Registration:
    • AiR: "Attention for Image Registration (AiR): an unsupervised Transformer approach", arXiv, 2021 (INRIA). [Paper]
  • Image Retrieval:
    • RRT: "Instance-level Image Retrieval using Reranking Transformers", ICCV, 2021 (University of Virginia). [Paper][PyTorch]
    • SwinFGHash: "SwinFGHash: Fine-grained Image Retrieval via Transformer-based Hashing Network", BMVC, 2021 (Tsinghua). [Paper]
    • ViT-Retrieval: "Investigating the Vision Transformer Model for Image Retrieval Tasks", arXiv, 2021 (Democritus University of Thrace). [Paper]
    • IRT: "Training Vision Transformers for Image Retrieval", arXiv, 2021 (Facebook + INRIA). [Paper]
    • TransHash: "TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval", arXiv, 2021 (Shanghai Jiao Tong University). [Paper]
    • VTS: "Vision Transformer Hashing for Image Retrieval", arXiv, 2021 (IIIT-Allahabad). [Paper]
    • GTZSR: "Zero-Shot Sketch Based Image Retrieval using Graph Transformer", arXiv, 2022 (IIT Bombay). [Paper]
  • Layout Generation:
    • VTN: "Variational Transformer Networks for Layout Generation", CVPR, 2021 (Google). [Paper]
    • LayoutTransformer: "LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity", CVPR, 2021 (NTU). [Paper][PyTorch]
    • LayoutTransformer: "LayoutTransformer: Layout Generation and Completion with Self-attention", ICCV, 2021 (Amazon). [Paper][Website]
    • LGT-Net: "LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network", CVPR, 2022 (East China Normal University). [Paper][PyTorch]
    • LayoutBERT: "LayoutBERT: Masked Language Layout Model for Object Insertion", CVPRW, 2022 (Adobe). [Paper]
    • ATEK: "ATEK: Augmenting Transformers with Expert Knowledge for Indoor Layout Synthesis", arXiv, 2022 (New Jersey Institute of Technology). [Paper]
  • Livestock Monitoring:
    • STARFormer: "Livestock Monitoring with Transformer", BMVC, 2021 (IIT Dhanbad). [Paper]
  • Long-tail:
    • BatchFormer: "BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning", CVPR, 2022 (The University of Sydney). [Paper][PyTorch]
    • BatchFormerV2: "BatchFormerV2: Exploring Sample Relationships for Dense Representation Learning", arXiv, 2022 (The University of Sydney). [Paper]
  • Metric Learning:
    • Hyp-ViT: "Hyperbolic Vision Transformers: Combining Improvements in Metric Learning", CVPR, 2022 (University of Trento, Italy). [Paper][PyTorch]
  • Multi-label:
    • C-Tran: "General Multi-label Image Classification with Transformers", CVPR, 2021 (University of Virginia). [Paper]
    • TDRG: "Transformer-Based Dual Relation Graph for Multi-Label Image Recognition", ICCV, 2021 (Tencent). [Paper]
    • MlTr: "MlTr: Multi-label Classification with Transformer", arXiv, 2021 (KuaiShou). [Paper]
    • GATN: "Graph Attention Transformer Network for Multi-Label Image Classification", arXiv, 2022 (Southeast University, China). [Paper]
  • Open Set:
    • OSR-ViT: "Open Set Recognition using Vision Transformer with an Additional Detection Head", arXiv, 2022 (Vanderbilt University, Tennessee). [Paper]
  • Out-Of-Distribution:
    • OODformer: "OODformer: Out-Of-Distribution Detection Transformer", BMVC, 2021 (LMU Munich). [Paper][PyTorch]
  • Pedestrian Intention:
    • IntFormer: "IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture", arXiv, 2021 (Universidad de Alcala). [Paper]
  • Place Recognition:
    • SVT-Net: "SVT-Net: A Super Light-Weight Network for Large Scale Place Recognition using Sparse Voxel Transformers", AAAI, 2022 (Renmin University of China). [Paper]
    • TransVPR: "TransVPR: Transformer-based place recognition with multi-level attention aggregation", arXiv, 2022 (Xi'an Jiaotong). [Paper]
  • Remote Sensing/Hyperspectral:
    • DCFAM: "Transformer Meets DCFAM: A Novel Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images", arXiv, 2021 (Wuhan University). [Paper]
    • WiCNet: "Looking Outside the Window: Wider-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images", arXiv, 2021 (University of Trento). [Paper]
    • ?: "Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images", arXiv, 2021 (University of Orleans, France). [Paper]
    • ?: "Self-supervised Vision Transformers for Joint SAR-optical Representation Learning", IGARSS, 2022 (German Aerospace Center). [Paper]
    • RNGDet: "RNGDet: Road Network Graph Detection by Transformer in Aerial Images", arXiv, 2022 (HKUST). [Paper]
    • FSRA: "A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization", arXiv, 2022 (China Jiliang University). [Paper][PyTorch]
    • ?: "Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classificationtion", arXiv, 2022 (Shenzhen University). [Paper]
    • ?: "Deep Hyperspectral Unmixing using Transformer Network", arXiv, 2022 (Jalpaiguri Engineering College, India). [Paper]
  • Robotics:
    • TF-Grasp: "When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection", arXiv, 2022 (University of Science and Technology of China). [Paper][Code (in construction)]
  • Satellite:
    • Satellite-ViT: "Manipulation Detection in Satellite Images Using Vision Transformer", arXiv, 2021 (Purdue). [Paper]
  • Scene Text Recognition:
    • ViTSTR: "Vision Transformer for Fast and Efficient Scene Text Recognition", ICDAR, 2021 (University of the Philippines). [Paper]
    • STKM: "Self-attention based Text Knowledge Mining for Text Detection", CVPR, 2021 (?). [Paper][Code (in construction)]
    • I2C2W: "I2C2W: Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition", arXiv, 2021 (NTU Singapoer). [Paper]
  • Stereo:
    • STTR: "Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers", ICCV, 2021 (Johns Hopkins). [Paper][PyTorch]
    • PS-Transformer: "PS-Transformer: Learning Sparse Photometric Stereo Network using Self-Attention Mechanism", BMVC, 2021 (National Institute of Informatics, JAPAN). [Paper]
    • ChiTransformer: "ChiTransformer: Towards Reliable Stereo from Cues", CVPR, 2022 (GSU). [Paper]
    • MVSTER: "MVSTER: Epipolar Transformer for Efficient Multi-View Stereo", arXiv, 2022 (CAS). [Paper][PyTorch]
  • Time Series:
    • MissFormer: "MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction", arXiv, 2021 (Fraunhofer IOSB, Germany). [Paper]
  • Traffic:
    • ViTAL: "Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder", IV, 2021 (Technische Hochschule Ingolstadt). [Paper]
    • ?: "Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information", IVS, 2021 (Universidad de Alcala). [Paper]
    • Crossview-Transformer: "Cross-view Transformers for real-time Map-view Semantic Segmentation", CVPR, 2022 (UT Austin). [Paper][PyTorch]
    • BEVSegFormer: "BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs", arXiv, 2022 (Nullmax, China). [Paper]
    • V2X-ViT: "V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer", arXiv, 2022 (UCLA). [Paper]
    • BEVFormer: "BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers", arXiv, 2022 (Shanghai AI Lab). [Paper][Code (in construction)]
    • ParkPredict+: "ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer", arXiv, 2022 (Berkeley). [Paper]
  • Trajectory Prediction:
    • mmTransformer: "Multimodal Motion Prediction with Stacked Transformers", CVPR, 2021 (CUHK + SenseTime). [Paper][Code (in construction)][Website]
    • AgentFormer: "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting", ICCV, 2021 (CMU). [Paper][Code (in construction)][Website]
    • MRT: "Multi-Person 3D Motion Prediction with Multi-Range Transformers", NeurIPS, 2021 (UCSD + Berkeley). [Paper][PyTorch][Website]
    • ?: "Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction", ICLR, 2022 (MILA). [Paper]
    • Scene-Transformer: "Scene Transformer: A unified architecture for predicting multiple agent trajectories", ICLR, 2022 (Google). [Paper]
    • LatentFormer: "LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction", arXiv, 2022 (Huawei). [Paper]
    • PreTR: "PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer", arXiv, 2022 (Stellantis, France). [Paper]
  • 3D Human Texture Estimation:
    • Texformer: "3D Human Texture Estimation from a Single Image with Transformers", ICCV, 2021 (NTU, Singapore). [Paper][PyTorch][Website]
  • 3D Motion Synthesis:
    • ACTOR: "Action-Conditioned 3D Human Motion Synthesis with Transformer VAE", ICCV, 2021 (Univ Gustave Eiffel). [Paper][PyTorch][Website]
    • ActFormer: "ActFormer: A GAN Transformer Framework towards General Action-Conditioned 3D Human Motion Generation", arXiv, 2022 (SenseTime). [Paper]
  • 3D Object Recognition:
    • MVT: "MVT: Multi-view Vision Transformer for 3D Object Recognition", BMVC, 2021 (Baidu). [Paper]
  • 3D Reconstruction:
    • PlaneTR: "PlaneTR: Structure-Guided Transformers for 3D Plane Recovery", ICCV, 2021 (Wuhan University). [Paper][PyTorch]
    • CO3D: "CommonObjects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction", ICCV, 2021 (Facebook). [Paper][PyTorch]
    • VolT: "Multi-view 3D Reconstruction with Transformer", ICCV, 2021 (University of British Columbia). [Paper]
    • 3D-RETR: "3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers", BMVC, 2021 (ETHZ). [Paper][PyTorch]
    • TransformerFusion: "TransformerFusion: Monocular RGB Scene Reconstruction using Transformers", NeurIPS, 2021 (TUM). [Paper][Website]
    • LegoFormer: "LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction", arXiv, 2021 (TUM + Google). [Paper]
  • 360 Scene:
    • ?: "Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning", AAAI, 2022 (Seoul National University). [Paper][PyTorch]
    • SPH: "Spherical Transformer", arXiv, 2022 (Chung-Ang University, Korea). [Paper]
    • PanoFormer: "PanoFormer: Panorama Transformer for Indoor 360° Depth Estimation", arXiv, 2022 (Beijing Jiaotong University). [Paper]

[Back to Overview]


Attention Mechanisms in Vision/NLP

Attention for Vision

  • AA: "Attention Augmented Convolutional Networks", ICCV, 2019 (Google). [Paper][PyTorch (Unofficial)][Tensorflow (Unofficial)]
  • LR-Net: "Local Relation Networks for Image Recognition", ICCV, 2019 (Microsoft). [Paper][PyTorch (Unofficial)]
  • CCNet: "CCNet: Criss-Cross Attention for Semantic Segmentation", ICCV, 2019 (& TPAMI 2020) (Horizon). [Paper][PyTorch]
  • GCNet: "Global Context Networks", ICCVW, 2019 (& TPAMI 2020) (Microsoft). [Paper][PyTorch]
  • SASA: "Stand-Alone Self-Attention in Vision Models", NeurIPS, 2019 (Google). [Paper][PyTorch-1 (Unofficial)][PyTorch-2 (Unofficial)]
    • key message: attention module is more efficient than conv & provide comparable accuracy
  • Axial-Transformer: "Axial Attention in Multidimensional Transformers", arXiv, 2019 (Google). [Paper][PyTorch (Unofficial)]
  • Attention-CNN: "On the Relationship between Self-Attention and Convolutional Layers", ICLR, 2020 (EPFL). [Paper][PyTorch][Website]
  • SAN: "Exploring Self-attention for Image Recognition", CVPR, 2020 (CUHK + Intel). [Paper][PyTorch]
  • BA-Transform: "Non-Local Neural Networks With Grouped Bilinear Attentional Transforms", CVPR, 2020 (ByteDance). [Paper]
  • Axial-DeepLab: "Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation", ECCV, 2020 (Google). [Paper][PyTorch]
  • GSA: "Global Self-Attention Networks for Image Recognition", arXiv, 2020 (Google). [Paper][PyTorch (Unofficial)]
  • EA: "Efficient Attention: Attention with Linear Complexities", WACV, 2021 (SenseTime). [Paper][PyTorch]
  • LambdaNetworks: "LambdaNetworks: Modeling long-range Interactions without Attention", ICLR, 2021 (Google). [Paper][PyTorch-1 (Unofficial)][PyTorch-2 (Unofficial)]
  • GSA-Nets: "Group Equivariant Stand-Alone Self-Attention For Vision", ICLR, 2021 (EPFL). [Paper]
  • Hamburger: "Is Attention Better Than Matrix Decomposition?", ICLR, 2021 (Peking). [Paper][PyTorch (Unofficial)]
  • HaloNet: "Scaling Local Self-Attention For Parameter Efficient Visual Backbones", CVPR, 2021 (Google). [Paper]
  • BoTNet: "Bottleneck Transformers for Visual Recognition", CVPR, 2021 (Google). [Paper]
  • SSAN: "SSAN: Separable Self-Attention Network for Video Representation Learning", CVPR, 2021 (Microsoft). [Paper]
  • CoTNet: "Contextual Transformer Networks for Visual Recognition", CVPRW, 2021 (JD). [Paper][PyTorch]
  • Involution: "Involution: Inverting the Inherence of Convolution for Visual Recognition", CVPR, 2021 (HKUST). [Paper][PyTorch]
  • Perceiver: "Perceiver: General Perception with Iterative Attention", ICML, 2021 (DeepMind). [Paper][PyTorch (lucidrains)]
  • SNL: "Unifying Nonlocal Blocks for Neural Networks", ICCV, 2021 (Peking + Bytedance). [Paper]
  • External-Attention: "Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks", arXiv, 2021 (Tsinghua). [Paper]
  • KVT: "KVT: k-NN Attention for Boosting Vision Transformers", arXiv, 2021 (Alibaba). [Paper]
  • Container: "Container: Context Aggregation Network", arXiv, 2021 (AI2). [Paper]
  • X-volution: "X-volution: On the unification of convolution and self-attention", arXiv, 2021 (Huawei Hisilicon). [Paper]
  • Invertible-Attention: "Invertible Attention", arXiv, 2021 (ANU). [Paper]
  • VOLO: "VOLO: Vision Outlooker for Visual Recognition", arXiv, 2021 (Sea AI Lab + NUS, Singapore). [Paper][PyTorch]
  • LESA: "Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms", arXiv, 2021 (Johns Hopkins). [Paper]
  • PS-Attention: "Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention", AAAI, 2022 (Baidu). [Paper][Paddle]
  • QuadTree: "QuadTree Attention for Vision Transformers", ICLR, 2022 (Simon Fraser + Alibaba). [Paper][PyTorch]
  • QnA: "Learned Queries for Efficient Local Attention", CVPR, 2022 (Tel-Aviv). [Paper][Jax]
  • HiP: "Hierarchical Perceiver", arXiv, 2022 (DeepMind). [Paper]

[Back to Overview]

Attention for NLP

  • T-DMCA: "Generating Wikipedia by Summarizing Long Sequences", ICLR, 2018 (Google). [Paper]
  • LSRA: "Lite Transformer with Long-Short Range Attention", ICLR, 2020 (MIT). [Paper][PyTorch]
  • ETC: "ETC: Encoding Long and Structured Inputs in Transformers", EMNLP, 2020 (Google). [Paper][Tensorflow]
  • BlockBERT: "Blockwise Self-Attention for Long Document Understanding", EMNLP Findings, 2020 (Facebook). [Paper][GitHub]
  • Clustered-Attention: "Fast Transformers with Clustered Attention", NeurIPS, 2020 (Idiap). [Paper][PyTorch][Website]
  • BigBird: "Big Bird: Transformers for Longer Sequences", NeurIPS, 2020 (Google). [Paper][Tensorflow]
  • Longformer: "Longformer: The Long-Document Transformer", arXiv, 2020 (AI2). [Paper][PyTorch]
  • Linformer: "Linformer: Self-Attention with Linear Complexity", arXiv, 2020 (Facebook). [Paper][PyTorch (Unofficial)]
  • Nystromformer: "Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention", AAAI, 2021 (UW-Madison). [Paper][PyTorch]
  • RFA: "Random Feature Attention", ICLR, 2021 (DeepMind). [Paper]
  • Performer: "Rethinking Attention with Performers", ICLR, 2021 (Google). [Paper][Code][Blog]
  • DeLight: "DeLighT: Deep and Light-weight Transformer", ICLR, 2021 (UW). [Paper]
  • Synthesizer: "Synthesizer: Rethinking Self-Attention for Transformer Models", ICML, 2021 (Google). [Paper][Tensorflow][Pytorch (leaderj1001)]
  • Poolingformer: "Poolingformer: Long Document Modeling with Pooling Attention", ICML, 2021 (Microsoft). [Paper]
  • Hi-Transformer: "Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling", ACL, 2021 (Tsinghua). [Paper]
  • Smart-Bird: "Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer", arXiv, 2021 (Tsinghua). [Paper]
  • Fastformer: "Fastformer: Additive Attention is All You Need", arXiv, 2021 (Tsinghua). [Paper]
  • ∞-former: "∞-former: Infinite Memory Transformer", arXiv, 2021 (Instituto de Telecomunicações, Portugal). [Paper]
  • cosFormer: "cosFormer: Rethinking Softmax In Attention", ICLR, 2022 (SenseTime). [Paper][PyTorch (davidsvy)]

[Back to Overview]

Attention for Both

  • Sparse-Transformer: "Generating Long Sequences with Sparse Transformers", arXiv, 2019 (OpenAI). [Paper][Tensorflow][Blog]
  • Reformer: "Reformer: The Efficient Transformer", ICLR, 2020 (Google). [Paper][Tensorflow][Blog]
  • Sinkhorn-Transformer: "Sparse Sinkhorn Attention", ICML, 2020 (Google). [Paper][PyTorch (Unofficial)]
  • Linear-Transformer: "Transformers are rnns: Fast autoregressive transformers with linear attention", ICML, 2020 (Idiap). [Paper][PyTorch][Website]
  • SMYRF: "SMYRF: Efficient Attention using Asymmetric Clustering", NeurIPS, 2020 (UT Austin + Google). [Paper][PyTorch]
  • Routing-Transformer: "Efficient Content-Based Sparse Attention with Routing Transformers", TACL, 2021 (Google). [Paper][Tensorflow][PyTorch (Unofficial)][Slides]
  • LRA: "Long Range Arena: A Benchmark for Efficient Transformers", ICLR, 2021 (Google). [Paper][Tensorflow]
  • OmniNet: "OmniNet: Omnidirectional Representations from Transformers", ICML, 2021 (Google). [Paper]
  • Evolving-Attention: "Evolving Attention with Residual Convolutions", ICML, 2021 (Peking + Microsoft). [Paper]
  • H-Transformer-1D: "H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences", ACL, 2021 (Google). [Paper]
  • Combiner: "Combiner: Full Attention Transformer with Sparse Computation Cost", NeurIPS, 2021 (Google). [Paper]
  • Centroid-Transformer: "Centroid Transformers: Learning to Abstract with Attention", arXiv, 2021 (UT Austin). [Paper]
  • AFT: "An Attention Free Transformer", arXiv, 2021 (Apple). [Paper]
  • Luna: "Luna: Linear Unified Nested Attention", arXiv, 2021 (USC + CMU + Facebook). [Paper]
  • Transformer-LS: "Long-Short Transformer: Efficient Transformers for Language and Vision", arXiv, 2021 (NVIDIA). [Paper]
  • PoNet: "PoNet: Pooling Network for Efficient Token Mixing in Long Sequences", ICLR, 2022 (Alibaba). [Paper]

[Back to Overview]

Attention for Others

  • Informer: "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting", AAAI, 2021 (Beihang University). [Paper][PyTorch]
  • Attention-Rank-Collapse: "Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth", ICML, 2021 (Google + EPFL). [Paper][PyTorch]
  • NPT: "Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning", arXiv, 2021 (Oxford). [Paper]

[Back to Overview]


Citation

If you find this repository useful, please consider citing this list:

@misc{chen2022transformerpaperlist,
    title = {Ultimate awesome paper list: transformer and attention},
    author = {Chen, Min-Hung},
    journal = {GitHub repository},
    url = {https://github.com/cmhungsteve/Awesome-Transformer-Attention},
    year = {2022},
}

References

  • Survey:
    • "A Survey on Visual Transformer", TPAMI, 2022 (Huawei). [Paper]
    • "A Comprehensive Study of Vision Transformers on Dense Prediction Tasks", VISAP, 2022 (NavInfo Europe, Netherlands). [Paper]
    • "Vision-and-Language Pretrained Models: A Survey", IJCAI, 2022 (The University of Sydney). [Paper]
    • "A survey on attention mechanisms for medical applications: are we moving towards better algorithms?", arXiv, 2022 (INESC TEC and University of Porto, Portugal). [Paper]
    • "Efficient Transformers: A Survey", arXiv, 2022 (Google). [Paper]
    • "Are we ready for a new paradigm shift? A Survey on Visual Deep MLP", arXiv, 2022 (Tsinghua). [Paper]
    • "Vision Transformers in Medical Computer Vision - A Contemplative Retrospection", arXiv, 2022 (National University of Sciences and Technology (NUST), Pakistan). [Paper]
    • "Video Transformers: A Survey", arXiv, 2022 (Universitat de Barcelona, Spain). [Paper]
    • "Transformers in Medical Image Analysis: A Review", arXiv, 2022 (Nanjing University). [Paper]
    • "Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work", arXiv, 2022 (?). [Paper]
    • "Transformers Meet Visual Learning Understanding: A Comprehensive Review", arXiv, 2022 (Xidian University). [Paper]
    • "Image Captioning In the Transformer Age", arXiv, 2022 (Alibaba). [Paper][GitHub]
    • "Visual Attention Methods in Deep Learning: An In-Depth Survey", arXiv, 2022 (Fayoum University, Egypt). [Paper]
    • "Transformers in Vision: A Survey", ACM Computing Surveys, 2021 (MBZUAI). [Paper]
    • "Survey: Transformer based Video-Language Pre-training", arXiv, 2021 (Renmin University of China). [Paper]
    • "A Survey of Transformers", arXiv, 2021 (Fudan). [Paper]
    • "A Survey of Visual Transformers", arXiv, 2021 (CAS). [Paper]
    • "Attention mechanisms and deep learning for machine vision: A survey of the state of the art", arXiv, 2021 (University of Kashmir, India). [Paper]
  • Online Resources:

About

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites