Cristand / CVPR2022-Papers-with-Code

CVPR 2022 论文和开源项目合集

CVPR 2022 论文和开源项目合集(Papers with Code)

CVPR 2022 论文和开源项目合集(papers with code)！

CVPR 2022 收录列表ID：https://drive.google.com/file/d/15JFhfPboKdUcIH9LdbCMUFmGq_JhaxhC/view

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注2：关于往年CV顶会论文以及其他优质CV论文和大盘点，详见： https://github.com/amusi/daily-paper-computer-vision

• CVPR 2019
• CVPR 2020
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【CVPR 2022 论文开源目录】

• Backbone
• CLIP
• NAS
• NeRF
• Visual Transformer
• 自监督学习(Self-supervised Learning)
• 数据增强(Data Augmentation)
• 目标检测(Object Detection)
• 目标跟踪(Visual Tracking)
• 语义分割(Semantic Segmentation)
• 实例分割(Instance Segmentation)
• 图像编辑(Image Editing)
• Low-level Vision
• 超分辨率(Super-Resolution)
• 3D点云(3D Point Cloud)
• 3D目标检测(3D Object Detection)
• 3D目标跟踪(3D Object Tracking)
• 3D人体姿态估计(3D Human Pose Estimation)
• 3D语义场景补全(3D Semantic Scene Completion)
• 3D重建(3D Reconstruction)
• 深度估计(Depth Estimation)
• 车道线检测(Lane Detection)
• 图像修复(Image Inpainting)
• 人群计数(Crowd Counting)
• 医学图像(Medical Image)
• 场景图生成(Scene Graph Generation)
• 水印(Watermarking)
• 数据集(Datasets)
• 新任务(New Tasks)
• 其他(Others)

Backbone

A ConvNet for the 2020s

• Paper: https://arxiv.org/abs/2201.03545

• Code: https://github.com/facebookresearch/ConvNeXt

• 中文解读：https://mp.weixin.qq.com/s/Xg5wPYExnvTqRo6s5-2cAw

MPViT : Multi-Path Vision Transformer for Dense Prediction

• Paper: https://arxiv.org/abs/2112.11010

• Code: https://github.com/youngwanLEE/MPViT

CLIP

HairCLIP: Design Your Hair by Text and Reference Image

• Paper: https://arxiv.org/abs/2112.05142

• Code: https://github.com/wty-ustc/HairCLIP

PointCLIP: Point Cloud Understanding by CLIP

• Paper: https://arxiv.org/abs/2112.02413
• Code: https://github.com/ZrrSkywalker/PointCLIP

Blended Diffusion for Text-driven Editing of Natural Images

• Paper: https://arxiv.org/abs/2111.14818

• Code: https://github.com/omriav/blended-diffusion

NAS

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

• Paper: https://arxiv.org/abs/2111.15362
• Code: None

NeRF

Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

• Homepage: https://jonbarron.info/mipnerf360/

• Paper: https://arxiv.org/abs/2111.12077

• Demo: https://youtu.be/YStDS2-Ln1s

Point-NeRF: Point-based Neural Radiance Fields

• Homepage: https://xharlie.github.io/projects/project_sites/pointnerf/
• Paper: https://arxiv.org/abs/2201.08845
• Code: https://github.com/Xharlie/point-nerf

NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images

• Paper: https://arxiv.org/abs/2111.13679

• Homepage: https://bmild.github.io/rawnerf/

• Demo: https://www.youtube.com/watch?v=JtBS4KBcKVc

Visual Transformer

Backbone

MPViT : Multi-Path Vision Transformer for Dense Prediction

• Paper: https://arxiv.org/abs/2112.11010

• Code: https://github.com/youngwanLEE/MPViT

应用

Language-based Video Editing via Multi-Modal Multi-Level Transformer

• Paper: https://arxiv.org/abs/2104.01122
• Code: None

MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video

• Paper: https://arxiv.org/abs/2203.00859
• Code: None

Embracing Single Stride 3D Object Detector with Sparse Transformer

• Paper: https://arxiv.org/abs/2112.06375
• Code: https://github.com/TuSimple/SST

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.02891
• Code: https://github.com/xulianuwa/MCTformer

自监督学习(Self-supervised Learning)

Crafting Better Contrastive Views for Siamese Representation Learning

• Paper: https://arxiv.org/abs/2202.03278

• Code: https://github.com/xyupeng/ContrastiveCrop

• 中文解读：https://mp.weixin.qq.com/s/VTP9D5f7KG9vg30U9kVI2A

数据增强(Data Augmentation)

TeachAugment: Data Augmentation Optimization Using Teacher Knowledge

• Paper: https://arxiv.org/abs/2202.12513
• Code: https://github.com/DensoITLab/TeachAugment

AlignMix: Improving representation by interpolating aligned features

• Paper: https://arxiv.org/abs/2103.15375
• Code: None

目标检测(Object Detection)

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

• Paper: https://arxiv.org/abs/2203.01305
• Code: https://github.com/FengLi-ust/DN-DETR

Localization Distillation for Dense Object Detection

• Paper: https://arxiv.org/abs/2102.12252
• Code: https://github.com/HikariTJU/LD
• Code2: https://github.com/HikariTJU/LD
• 中文解读：https://mp.weixin.qq.com/s/dxss8RjJH283h6IbPCT9vg

Focal and Global Knowledge Distillation for Detectors

• Paper: https://arxiv.org/abs/2111.11837

• Code: https://github.com/yzd-v/FGD

• 中文解读：https://mp.weixin.qq.com/s/yDkreTudC8JL2V2ETsADwQ

目标跟踪(Visual Tracking)

Correlation-Aware Deep Tracking

• Paper: https://arxiv.org/abs/2203.01666
• Code: None

TCTrack: Temporal Contexts for Aerial Tracking

• Paper: https://arxiv.org/abs/2203.01885

• Code: https://github.com/vision4robotics/TCTrack

语义分割(Semantic Segmentation)

弱监督语义分割

Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.00962
• Code: https://github.com/zhaozhengChen/ReCAM

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.02891
• Code: https://github.com/xulianuwa/MCTformer

半监督语义分割

ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

• Paper: https://arxiv.org/abs/2106.05095
• Code: https://github.com/LiheYoung/ST-PlusPlus
• 中文解读：https://mp.weixin.qq.com/s/knSnlebdtEnmrkChGM_0CA

实例分割(Instance Segmentation)

E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

• Paper: https://arxiv.org/abs/2203.04074
• Code: https://github.com/zhang-tao-whu/e2ec

自监督实例分割

FreeSOLO: Learning to Segment Objects without Annotations

• Paper: https://arxiv.org/abs/2202.12181
• Code: None

视频实例分割

Efficient Video Instance Segmentation via Tracklet Query and Proposal

• Homepage: https://jialianwu.com/projects/EfficientVIS.html
• Paper: https://arxiv.org/abs/2203.01853
• Demo: https://youtu.be/sSPMzgtMKCE

图像编辑(Image Editing)

Blended Diffusion for Text-driven Editing of Natural Images

• Paper: https://arxiv.org/abs/2111.14818

• Code: https://github.com/omriav/blended-diffusion

Low-level Vision

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

• Paper: https://arxiv.org/abs/2111.15362
• Code: None

超分辨率(Super-Resolution)

视频超分辨率

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

• Paper: https://arxiv.org/abs/2104.13371
• Code: https://github.com/open-mmlab/mmediting
• Code: https://github.com/ckkelvinchan/BasicVSR_PlusPlus
• 中文解读：https://mp.weixin.qq.com/s/HZTwYfphixyLHxlbCAxx4g

3D点云(3D Point Cloud)

A Unified Query-based Paradigm for Point Cloud Understanding

• Paper: https://arxiv.org/abs/2203.01252
• Code: None

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding

• Paper: https://arxiv.org/abs/2203.00680
• Code: https://github.com/MohamedAfham/CrossPoint

PointCLIP: Point Cloud Understanding by CLIP

• Paper: https://arxiv.org/abs/2112.02413
• Code: https://github.com/ZrrSkywalker/PointCLIP

3D目标检测(3D Object Detection)

Embracing Single Stride 3D Object Detector with Sparse Transformer

• Paper: https://arxiv.org/abs/2112.06375

• Code: https://github.com/TuSimple/SST

Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes

• Paper: https://arxiv.org/abs/2011.12001
• Code: https://github.com/qq456cvb/CanonicalVoting

3D目标跟踪(3D Object Tracking)

Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds

• Paper: https://arxiv.org/abs/2203.01730
• Code: https://github.com/Ghostish/Open3DSOT

3D人体姿态估计(3D Human Pose Estimation)

MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation

• Paper: https://arxiv.org/abs/2111.12707

• Code: https://github.com/Vegetebird/MHFormer

• 中文解读: https://zhuanlan.zhihu.com/p/439459426

MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video

• Paper: https://arxiv.org/abs/2203.00859
• Code: None

3D语义场景补全(3D Semantic Scene Completion)

MonoScene: Monocular 3D Semantic Scene Completion

• Paper: https://arxiv.org/abs/2112.00726
• Code: https://github.com/cv-rits/MonoScene

3D重建(3D Reconstruction)

BANMo: Building Animatable 3D Neural Models from Many Casual Videos

• Homepage: https://banmo-www.github.io/
• Paper: https://arxiv.org/abs/2112.12761
• Code: https://github.com/facebookresearch/banmo
• 中文解读：https://mp.weixin.qq.com/s/NMHP8-xWwrX40vpGx55Qew

深度估计(Depth Estimation)

单目深度估计

NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

• Paper: https://arxiv.org/abs/2203.01502
• Code: None

OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion

• Paper: https://arxiv.org/abs/2203.00838
• Code: None

Toward Practical Self-Supervised Monocular Indoor Depth Estimation

• Paper: https://arxiv.org/abs/2112.02306
• Code: None

车道线检测(Lane Detection)

Rethinking Efficient Lane Detection via Curve Modeling

• Paper: https://arxiv.org/abs/2203.02431

• Code: https://github.com/voldemortX/pytorch-auto-drive

• Demo：https://user-images.githubusercontent.com/32259501/148680744-a18793cd-f437-461f-8c3a-b909c9931709.mp4

图像修复(Image Inpainting)

Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding

• Paper: https://arxiv.org/abs/2203.00867

• Code: https://github.com/DQiaole/ZITS_inpainting

人群计数(Crowd Counting)

Leveraging Self-Supervision for Cross-Domain Crowd Counting

• Paper: https://arxiv.org/abs/2103.16291
• Code: None

医学图像(Medical Image)

BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation

• Paper: https://arxiv.org/abs/2203.02533
• Code: None

场景图生成(Scene Graph Generation)

SGTR: End-to-end Scene Graph Generation with Transformer

• Paper: https://arxiv.org/abs/2112.12970
• Code: None

风格迁移(Style Transfer)

StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions

• Homepage: https://lukashoel.github.io/stylemesh/

• Paper: https://arxiv.org/abs/2112.01530

• Code: https://github.com/lukasHoel/stylemesh

• Demo：https://www.youtube.com/watch?v=ZqgiTLcNcks

水印(Watermarking)

Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings

• Paper: https://arxiv.org/abs/2104.13450
• Code: None

数据集(Datasets)

It's About Time: Analog Clock Reading in the Wild

• Homepage: https://charigyang.github.io/abouttime/
• Paper: https://arxiv.org/abs/2111.09162
• Code: https://github.com/charigyang/itsabouttime
• Demo: https://youtu.be/cbiMACA6dRc

Toward Practical Self-Supervised Monocular Indoor Depth Estimation

• Paper: https://arxiv.org/abs/2112.02306
• Code: None

Kubric: A scalable dataset generator

• Paper: https://arxiv.org/abs/2203.03570

• Code: https://github.com/google-research/kubric

新任务(New Task)

Language-based Video Editing via Multi-Modal Multi-Level Transformer

• Paper: https://arxiv.org/abs/2104.01122
• Code: None

It's About Time: Analog Clock Reading in the Wild

• Homepage: https://charigyang.github.io/abouttime/

• Paper: https://arxiv.org/abs/2111.09162

• Code: https://github.com/charigyang/itsabouttime

• Demo: https://youtu.be/cbiMACA6dRc

其他(Others)

Kubric: A scalable dataset generator

• Paper: https://arxiv.org/abs/2203.03570

• Code: https://github.com/google-research/kubric