- 官网链接:http://cvpr2021.thecvf.com
- 时间:2021年6月19日-6月25日
- 论文接收公布时间:2021年2月28日
- CVPR2021官方接受论文列表:http://cvpr2021.thecvf.com/sites/default/files/2021-03/accepted_paper_ids.txt
- 1.超分辨率(Super-Resolution)
- 2.图像去雨(Image Deraining)
- 3.图像去雾(Image Dehazing)
- 4.去模糊(Deblurring)
- 5.去噪(Denoising)
- 6.图像恢复(Image Restoration)
- 7.图像增强(Image Enhancement)
- 8.图像去摩尔纹(Image Demoireing)
- 9.图像阴影去除(Image Shadow Removal)
- 10.图像翻译(Image Translation)
- 11.插帧(Frame Interpolation)
- 12.视频压缩(Video Compression)
- 13.图像编辑(Image Edit)
- 图像目标检测(Image Object Detection)
- 视频目标检测(Video Object Detection)
- 三维目标检测(3D Object Detection)
- 动作检测(Activity Detection)
- 异常检测(Anomally Detetion)
- Paper:https://arxiv.org/abs/2011.14631
- Homepage:http://www.liuyebin.com/crossMPI/crossMPI.html
- Analysis:CVPR 2021,Cross-MPI以底层场景结构为线索的端到端网络,在大分辨率(x8)差距下也可完成高保真的超分辨率
CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models
- 论文地址:https://arxiv.org/pdf/2009.08891.pdf
- 代码地址:https://github.com/huawei-noah/AdderNet
- 解读:华为开源加法神经网络
- Paper:https://arxiv.org/abs/2103.01255
- Code:https://github.com/tsingqguo/exposure-fusion-shadow-removal
- Paper:https://arxiv.org/abs/2012.08512
- Code:https://tarun005.github.io/FLAVR/Code
- Homepage:https://tarun005.github.io/FLAVR/
[1] Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection(小样本目标检测的语义关系推理)
[2] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
- 解读:无监督预训练检测器
[3] Positive-Unlabeled Data Purification in the Wild for Object Detection(野外检测对象的阳性无标签数据提纯)
[4] General Instance Distillation for Object Detection(通用实例蒸馏技术在目标检测中的应用)
[5] Instance Localization for Self-supervised Detection Pretraining(自监督检测预训练的实例定位)
[6] Multiple Instance Active Learning for Object Detection(用于对象检测的多实例主动学习)
[7] Towards Open World Object Detection(开放世界中的目标检测)
[1] Depth from Camera Motion and Object Detection(相机运动和物体检测的深度)
[2] There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge(多模态知识提取的自监督多目标检测与有声跟踪)
[3] Dogfight: Detecting Drones from Drone Videos(从无人机视频中检测无人机)
[1] 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection(利用IoU预测进行半监督3D对象检测)
[2] Categorical Depth Distribution Network for Monocular 3D Object Detection(用于单目三维目标检测的分类深度分布网络)
[1] Coarse-Fine Networks for Temporal Activity Detection in Videos
[1] Multiresolution Knowledge Distillation for Anomaly Detection(用于异常检测的多分辨率知识蒸馏)
[1] Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
[2] PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation(语义流经点以进行航空图像分割)
[1] Cross-View Regularization for Domain Adaptive Panoptic Segmentation(用于域自适应全景分割的跨视图正则化)
[2] 4D Panoptic LiDAR Segmentation(4D全景LiDAR分割)
[1] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市规模3D点云的语义分割:数据集,基准和挑战)
[2] PLOP: Learning without Forgetting for Continual Semantic Segmentation(PLOP:学习而不会忘记连续的语义分割) paper
### 实例分割(Instance Segmentation)[1] End-to-End Video Instance Segmentation with Transformers(使用Transformer的端到端视频实例分割)
[1] Real-Time High Resolution Background Matting
[1] CanonPose: Self-supervised Monocular 3D Human Pose Estimation in the Wild(野外自监督的单眼3D人类姿态估计)
[2] PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers(具有透视作物层的3D姿势的几何感知神经重建)
[1] Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration(基于语义聚合和自适应2D-1D配准的相机空间手部网格恢复)
[1] GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation(用于单眼6D对象姿态估计的几何引导直接回归网络)
[2] Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments(在动态室内环境中,通过空间划分的鲁棒神经路由可实现摄像机的重新定位)
[3] MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization(通过3D扫描同步进行多主体分割和运动估计)
[1] Cross Modal Focal Loss for RGBD Face Anti-Spoofing(Cross Modal Focal Loss for RGBD Face Anti-Spoofing)
[2] When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework(当年龄不变的人脸识别遇到人脸年龄合成时:一个多任务学习框架)
[3] Multi-attentional Deepfake Detection(多注意的深伪检测)
[4] Image-to-image Translation via Hierarchical Style Disentanglement
[5] A 3D GAN for Improved Large-pose Facial Recognition(用于改善大姿势面部识别的3D GAN)
[4] HPS: localizing and tracking people in large 3D scenes from wearable sensors(通过可穿戴式传感器对大型3D场景中的人进行定位和跟踪)
[3] Track to Detect and Segment: An Online Multi-Object Tracker(跟踪检测和分段:在线多对象跟踪器)
[2] Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking(多目标跟踪的概率小波计分和修复)
[1] Rotation Equivariant Siamese Networks for Tracking(旋转等距连体网络进行跟踪)
[1] Meta Batch-Instance Normalization for Generalizable Person Re-Identification(通用批处理人员重新标识的元批实例规范化)
[5] DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images(一种心脏标记磁共振图像运动跟踪的无监督深度学习方法)
[4] Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning(多机构协作改进基于深度学习的联合学习磁共振图像重建)
[3] 3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management(用于胰腺肿块分割,诊断和定量患者管理的3D图形解剖学几何集成网络)
[2] Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies(深部病变追踪器:在4D纵向成像研究中监控病变)
[1] Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization(通过脊柱矫正和解剖学约束优化在CT中自动进行椎骨定位和识别)
[3] AttentiveNAS: Improving Neural Architecture Search via Attentive(通过注意力改善神经架构搜索)
[2] ReNAS: Relativistic Evaluation of Neural Architecture Search(NAS predictor当中ranking loss的重要性)
[1] HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens(降低NAS的成本)
[6] Anycost GANs for Interactive Image Synthesis and Editing(用于交互式图像合成和编辑的AnyCost Gans)
[5] Efficient Conditional GAN Transfer with Knowledge Propagation across Classes(高效的有条件GAN转移以及跨课程的知识传播)
[4] Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing(利用GAN中潜在的空间维度进行实时图像编辑)
[3] Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs(Hijack-GAN:意外使用经过预训练的黑匣子GAN)
[2] Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation(样式编码:用于图像到图像翻译的StyleGAN编码器)
[1] A 3D GAN for Improved Large-pose Facial Recognition(用于改善大姿势面部识别的3D GAN)
[2] A Deep Emulator for Secondary Motion of 3D Characters(三维角色二次运动的深度仿真器)
[1] 3D CNNs with Adaptive Temporal Feature Resolutions(具有自适应时间特征分辨率的3D CNN)
[8] TPCN: Temporal Point Cloud Networks for Motion Forecasting(面向运动预测的时态点云网络)
[7] PointGuard: Provably Robust 3D Point Cloud Classification(可证明稳健的三维点云分类)
[6] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市规模3D点云的语义分割:数据集,基准和挑战)
[5] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration(SpinNet:学习用于3D点云注册的通用表面描述符)
[4] MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization(通过3D扫描同步进行多主体分割和运动估计)
[3] Diffusion Probabilistic Models for 3D Point Cloud Generation(三维点云生成的扩散概率模型)
[2] Style-based Point Generator with Adversarial Rendering for Point Cloud Completion(用于点云补全的对抗性渲染基于样式的点生成器)
[1] PREDATOR: Registration of 3D Point Clouds with Low Overlap(预测器:低重叠的3D点云的注册)
[1] PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers(具有透视作物层的3D姿势的几何感知神经重建)
[1] Manifold Regularized Dynamic Network Pruning(动态剪枝的过程中考虑样本复杂度与网络复杂度的约束)
[2] Learning Student Networks in the Wild(一种不需要原始训练数据的模型压缩和加速技术)
[3] General Instance Distillation for Object Detection(通用实例蒸馏技术在目标检测中的应用)
[2] Multiresolution Knowledge Distillation for Anomaly Detection(用于异常检测的多分辨率知识蒸馏)
[1] Distilling Object Detectors via Decoupled Features(前景背景分离的蒸馏技术)
[1] Coordinate Attention for Efficient Mobile Network Design(协调注意力以实现高效的移动网络设计)
[2] Inception Convolution with Efficient Dilation Search
- Paper: https://arxiv.org/abs/2012.13587
- Code: None
[3] Rethinking Channel Dimensions for Efficient Model Design(重新考虑通道尺寸以进行有效的模型设计)
[4] Inverting the Inherence of Convolution for Visual Recognition(颠倒卷积的固有性以进行视觉识别)
[5] RepVGG: Making VGG-style ConvNets Great Again
[1] Transformer Interpretability Beyond Attention Visualization(注意力可视化之外的Transformer可解释性)
[2] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
[3] Pre-Trained Image Processing Transformer(底层视觉预训练模型)
[2] Quantifying Explainers of Graph Neural Networks in Computational Pathology(计算病理学中图神经网络的量化解释器)
[1] Sequential Graph Convolutional Network for Active Learning(主动学习的顺序图卷积网络)
[1] KeepAugment: A Simple Information-Preserving Data Augmentation(一种简单的保存信息的数据扩充)
[3] Adaptive Consistency Regularization for Semi-Supervised Transfer Learning(半监督转移学习的自适应一致性正则化)
[2] Meta Batch-Instance Normalization for Generalizable Person Re-Identification(通用批处理人员重新标识的元批实例规范化)
[1] Representative Batch Normalization with Feature Calibration(具有特征校准功能的代表性批量归一化)
[2] Improving Unsupervised Image Clustering With Robust Learning(通过鲁棒学习改善无监督图像聚类)
[1] Reconsidering Representation Alignment for Multi-view Clustering(重新考虑多视图聚类的表示对齐方式)
[1] Are Labels Necessary for Classifier Accuracy Evaluation?(测试集没有标签,我们可以拿来测试模型吗?)
[2] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市规模3D点云的语义分割:数据集,基准和挑战)
[1] Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels(重新标记ImageNet:从单标签到多标签,从全局标签到本地标签)
[3] Vab-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
[2] Multiple Instance Active Learning for Object Detection(用于对象检测的多实例主动学习)
[1] Sequential Graph Convolutional Network for Active Learning(主动学习的顺序图卷积网络)
[5] Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
[4] Counterfactual Zero-Shot and Open-Set Visual Recognition(反事实零射和开集视觉识别)
[3] Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection(小样本目标检测的语义关系推理)
[2] Few-shot Open-set Recognition by Transformation Consistency(转换一致性很少的开放集识别)
[1] Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning(探索少量学习的不变表示形式和等变表示形式的互补强度)
[2] Rainbow Memory: Continual Learning with a Memory of Diverse Samples(不断学习与多样本的记忆)
[1] Learning the Superpixel in a Non-iterative and Lifelong Manner(以非迭代和终身的方式学习超像素)
[1] Transformation Driven Visual Reasoning(转型驱动的视觉推理)
[4] Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning(通过域随机化和元学习对视觉表示进行连续调整)
[3] Domain Generalization via Inference-time Label-Preserving Target Projections(基于推理时间保标目标投影的区域泛化)
[2] MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing(可伸缩的自适应视频压缩传感重建)
[1] FSDR: Frequency Space Domain Randomization for Domain Generalization(用于域推广的频域随机化)
[1] Fine-grained Angular Contrastive Learning with Coarse Labels(粗标签的细粒度角度对比学习)
[1] QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval(实用的查询高效的图像检索黑盒攻击)
Learning Asynchronous and Sparse Human-Object Interaction in Videos(视频中异步稀疏人-物交互的学习)
Self-supervised Geometric Perception(自我监督的几何知觉)
Quantifying Explainers of Graph Neural Networks in Computational Pathology(计算病理学中图神经网络的量化解释器)
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts(探索具有对比场景上下文的数据高效3D场景理解)
Data-Free Model Extraction(无数据模型提取)
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition(用于【位置识别】的局部全局描述符的【多尺度融合】)
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations(适用于正确概念的权利:通过可解释性来修正神经符号概念)
Multi-Objective Interpolation Training for Robustness to Label Noise(多目标插值训练的鲁棒性)
VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs(【文本生成】VX2TEXT:基于视频的文本生成的端到端学习来自多模式输入)
Scan2Cap: Context-aware Dense Captioning in RGB-D Scans(【图像字幕】Scan2Cap:RGB-D扫描中的上下文感知密集字幕)
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph(基于目标关系图的分层部分可观测目标驱动策略学习)
ID-Unet: Iterative Soft and Hard Deformation for View Synthesis(视图合成的迭代软硬变形)
PML: Progressive Margin Loss for Long-tailed Age Classification(【长尾分布】【图像分类】长尾年龄分类的累进边际损失)
Diversifying Sample Generation for Data-Free Quantization(【图像生成】多样化的样本生成,实现无数据量化)
Domain Generalization via Inference-time Label-Preserving Target Projections(通过保留推理时间的目标投影进行域泛化)
DeRF: Decomposed Radiance Fields(分解的辐射场)
Densely connected multidilated convolutional networks for dense prediction tasks(【密集预测】密集连接的多重卷积网络,用于密集的预测任务)
VirTex: Learning Visual Representations from Textual Annotations(【表示学习】从文本注释中学习视觉表示)
Weakly-supervised Grounded Visual Question Answering using Capsules(使用胶囊进行弱监督的地面视觉问答)
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation(【视频插帧】FLAVR:用于快速帧插值的与流无关的视频表示)
Probabilistic Embeddings for Cross-Modal Retrieval(跨模态检索的概率嵌入)
Self-supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map(道路动力学和成本图的自监督式多步同时预测)
IIRC: Incremental Implicitly-Refined Classification(增量式隐式定义的分类)
Fair Attribute Classification through Latent Space De-biasing(通过潜在空间去偏的公平属性分类)
Information-Theoretic Segmentation by Inpainting Error Maximization(修复误差最大化的信息理论分割)
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pretraining(【视频语言学习】UC2:通用跨语言跨模态视觉和语言预培训)
Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling(通过稀疏采样进行视频和语言学习)
D-NeRF: Neural Radiance Fields for Dynamic Scenes(D-NeRF:动态场景的神经辐射场)
Weakly Supervised Learning of Rigid 3D Scene Flow(刚性3D场景流的弱监督学习)
[23] Self-supervised Geometric Perception(自我监督的几何知觉)
[22] DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images(一种心脏标记磁共振图像运动跟踪的无监督深度学习方法)
[21] Modeling Multi-Label Action Dependencies for Temporal Action Localization(为时间动作本地化建模多标签动作相关性)
[20] HPS: localizing and tracking people in large 3D scenes from wearable sensors(通过可穿戴式传感器对大型3D场景中的人进行定位和跟踪)
[19] Real-Time High Resolution Background Matting(实时高分辨率背景抠像)
[18] Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts(探索具有对比场景上下文的数据高效3D场景理解)
[17] Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments(在动态室内环境中,通过空间划分的鲁棒神经路由可实现摄像机的重新定位)
[16] MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization(通过3D扫描同步进行多主体分割和运动估计)
[15] Categorical Depth Distribution Network for Monocular 3D Object Detection(用于单目三维目标检测的分类深度分布网络)
[14] PatchmatchNet: Learned Multi-View Patchmatch Stereo(学习多视图立体声)
[13] Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning(通过域随机化和元学习对视觉表示进行连续调整)
[12] Single-Stage Instance Shadow Detection with Bidirectional Relation Learning(具有双向关系学习的单阶段实例阴影检测)
[11] Neural Geometric Level of Detail:Real-time Rendering with Implicit 3D Surfaces(神经几何细节水平:隐式3D曲面的实时渲染)
[9] PREDATOR: Registration of 3D Point Clouds with Low Overlap(预测器:低重叠的3D点云的注册)
[8] Domain Generalization via Inference-time Label-Preserving Target Projections(通过保留推理时间的目标投影进行域泛化)
[7] Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction(全局一致的非刚性重建的神经变形图)
[6] Fine-grained Angular Contrastive Learning with Coarse Labels(粗标签的细粒度角度对比学习)
[5] Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling(通过稀疏采样进行视频和语言学习)
[4] Cross-View Regularization for Domain Adaptive Panoptic Segmentation(用于域自适应全景分割的跨视图正则化)
[3] Image-to-image Translation via Hierarchical Style Disentanglement(通过分层样式分解实现图像到图像的翻译)
[2] Towards Open World Object Detection(开放世界中的目标检测)
- [paper](Towards Open World Object Detection)
- code
[1] End-to-End Video Instance Segmentation with Transformers(使用Transformer的端到端视频实例分割)
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