CVPR2022-Papers-with-Code.md

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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【CVPR 2022 论文开源目录】

• Backbone
• CLIP
• GAN
• GNN
• MLP
• NAS
• OCR
• NeRF
• [3D Face](#3D Face)
• 长尾分布(Long-Tail)
• Visual Transformer
• 视觉和语言(Vision-Language)
• 自监督学习(Self-supervised Learning)
• 数据增强(Data Augmentation)
• 知识蒸馏(Knowledge Distillation)
• 目标检测(Object Detection)
• 目标跟踪(Visual Tracking)
• 语义分割(Semantic Segmentation)
• 实例分割(Instance Segmentation)
• 全景分割(Panoptic Segmentation)
• 小样本分类(Few-Shot Classification)
• 小样本分割(Few-Shot Segmentation)
• 图像抠图(Image Matting)
• 视频理解(Video Understanding)
• 图像编辑(Image Editing)
• Low-level Vision
• 超分辨率(Super-Resolution)
• 去模糊(Deblur)
• 3D点云(3D Point Cloud)
• 3D目标检测(3D Object Detection)
• 3D语义分割(3D Semantic Segmentation)
• 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)
• 行人重识别(Person Re-identification)
• 伪装物体检测(Camouflaged Object Detection)
• 深度估计(Depth Estimation)
• 立体匹配(Stereo Matching)
• 特征匹配(Feature Matching)
• 车道线检测(Lane Detection)
• 光流估计(Optical Flow Estimation)
• 图像修复(Image Inpainting)
• 图像检索(Image Retrieval)
• 人脸识别(Face Recognition)
• 人群计数(Crowd Counting)
• 医学图像(Medical Image)
• [视频生成(Video Generation)](#Video Generation)
• 场景图生成(Scene Graph Generation)
• 参考视频目标分割(Referring Video Object Segmentation)
• 步态识别(Gait Recognition)
• 风格迁移(Style Transfer)
• 异常检测(Anomaly Detection
• 对抗样本(Adversarial Examples)
• 弱监督物体检测(Weakly Supervised Object Localization)
• 雷达目标检测(Radar Object Detection)
• 高光谱图像重建(Hyperspectral Image Reconstruction)
• 图像拼接(Image Stitching)
• 水印(Watermarking)
• Action Counting
• Grounded Situation Recognition
• Zero-shot Learning
• DeepFakes
• 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

Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

• Paper: https://arxiv.org/abs/2203.06717

• Code: https://github.com/megvii-research/RepLKNet

• Code2: https://github.com/DingXiaoH/RepLKNet-pytorch

• 中文解读：https://mp.weixin.qq.com/s/_qXyIQut-JRW6VvsjaQlFg

MPViT : Multi-Path Vision Transformer for Dense Prediction

• Paper: https://arxiv.org/abs/2112.11010
• Code: https://github.com/youngwanLEE/MPViT
• 中文解读: https://mp.weixin.qq.com/s/Q9-crEOz5IYzZaNoq8oXfg

Mobile-Former: Bridging MobileNet and Transformer

• Paper: https://arxiv.org/abs/2108.05895
• Code: None
• 中文解读：https://mp.weixin.qq.com/s/yo5KmB2Y7t2R4jiOKI87HQ

MetaFormer is Actually What You Need for Vision

• Paper: https://arxiv.org/abs/2111.11418
• Code: https://github.com/sail-sg/poolformer

Shunted Self-Attention via Multi-Scale Token Aggregation

• Paper(Oral): https://arxiv.org/abs/2111.15193
• Code: https://github.com/OliverRensu/Shunted-Transformer

TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing

• Paper: http://arxiv.org/abs/2203.10489
• Code: https://github.com/JierunChen/TVConv

Learned Queries for Efficient Local Attention

• Paper(Oral): https://arxiv.org/abs/2112.11435
• Code: https://github.com/moabarar/qna

RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality

• Paper: https://arxiv.org/abs/2112.11081
• Code: https://github.com/DingXiaoH/RepMLP

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

GAN

SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing

• Homepage: https://semanticstylegan.github.io/

• Paper: https://arxiv.org/abs/2112.02236

• Demo: https://semanticstylegan.github.io/videos/demo.mp4

Style Transformer for Image Inversion and Editing

• Paper: https://arxiv.org/abs/2203.07932
• Code: https://github.com/sapphire497/style-transformer

Unsupervised Image-to-Image Translation with Generative Prior

• Homepage: https://www.mmlab-ntu.com/project/gpunit/
• Paper: https://arxiv.org/abs/2204.03641
• Code: https://github.com/williamyang1991/GP-UNIT

StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2

• Homepage: https://universome.github.io/stylegan-v
• Paper: https://arxiv.org/abs/2112.14683
• Code: https://github.com/universome/stylegan-v

OSSGAN: Open-set Semi-supervised Image Generation

• Paper: https://arxiv.org/abs/2204.14249
• Code: https://github.com/raven38/OSSGAN

Neural Texture Extraction and Distribution for Controllable Person Image Synthesis

• Paper: https://arxiv.org/abs/2204.06160
• Code: https://github.com/RenYurui/Neural-Texture-Extraction-Distribution

GNN

OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks

• Paper: https://wanyu-lin.github.io/assets/publications/wanyu-cvpr2022.pdf
• Code: https://github.com/WanyuGroup/CVPR2022-OrphicX

MLP

RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality

• Paper: https://arxiv.org/abs/2112.11081
• Code: https://github.com/DingXiaoH/RepMLP

NAS

β-DARTS: Beta-Decay Regularization for Differentiable Architecture Search

• Paper: https://arxiv.org/abs/2203.01665
• Code: https://github.com/Sunshine-Ye/Beta-DARTS

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

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

OCR

SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition

• Paper: https://arxiv.org/abs/2203.10209

• Code: https://github.com/mxin262/SwinTextSpotter

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

Urban Radiance Fields

• Homepage: https://urban-radiance-fields.github.io/

• Paper: https://arxiv.org/abs/2111.14643

• Demo: https://youtu.be/qGlq5DZT6uc

Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translation

• Paper: https://arxiv.org/abs/2202.13162
• Code: https://github.com/HexagonPrime/Pix2NeRF

HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video

• Homepage: https://grail.cs.washington.edu/projects/humannerf/

• Paper: https://arxiv.org/abs/2201.04127

• Demo: https://youtu.be/GM-RoZEymmw

3D Face

ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations

• Paper: https://arxiv.org/abs/2203.14510
• Code: https://github.com/MingwuZheng/ImFace

长尾分布(Long-Tail)

Retrieval Augmented Classification for Long-Tail Visual Recognition

• Paper: https://arxiv.org/abs/2202.11233
• Code: None

Visual Transformer

Backbone

MPViT : Multi-Path Vision Transformer for Dense Prediction

• Paper: https://arxiv.org/abs/2112.11010
• Code: https://github.com/youngwanLEE/MPViT

MetaFormer is Actually What You Need for Vision

• Paper: https://arxiv.org/abs/2111.11418
• Code: https://github.com/sail-sg/poolformer

Mobile-Former: Bridging MobileNet and Transformer

• Paper: https://arxiv.org/abs/2108.05895
• Code: None
• 中文解读：https://mp.weixin.qq.com/s/yo5KmB2Y7t2R4jiOKI87HQ

Shunted Self-Attention via Multi-Scale Token Aggregation

• Paper(Oral): https://arxiv.org/abs/2111.15193
• Code: https://github.com/OliverRensu/Shunted-Transformer

Learned Queries for Efficient Local Attention

• Paper(Oral): https://arxiv.org/abs/2112.11435
• Code: https://github.com/moabarar/qna

应用(Application)

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
• 中文解读：https://zhuanlan.zhihu.com/p/476056546

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

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

Spatio-temporal Relation Modeling for Few-shot Action Recognition

• Paper: https://arxiv.org/abs/2112.05132
• Code: https://github.com/Anirudh257/strm

Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction

• Paper: https://arxiv.org/abs/2111.07910
• Code: https://github.com/caiyuanhao1998/MST

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

• Homepage: https://point-bert.ivg-research.xyz/
• Paper: https://arxiv.org/abs/2111.14819
• Code: https://github.com/lulutang0608/Point-BERT

GroupViT: Semantic Segmentation Emerges from Text Supervision

• Homepage: https://jerryxu.net/GroupViT/

• Paper: https://arxiv.org/abs/2202.11094

• Demo: https://youtu.be/DtJsWIUTW-Y

Restormer: Efficient Transformer for High-Resolution Image Restoration

• Paper: https://arxiv.org/abs/2111.09881
• Code: https://github.com/swz30/Restormer

Splicing ViT Features for Semantic Appearance Transfer

• Homepage: https://splice-vit.github.io/
• Paper: https://arxiv.org/abs/2201.00424
• Code: https://github.com/omerbt/Splice

Self-supervised Video Transformer

• Homepage: https://kahnchana.github.io/svt/

• Paper: https://arxiv.org/abs/2112.01514

• Code: https://github.com/kahnchana/svt

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers

• Paper: https://arxiv.org/abs/2203.02664
• Code: https://github.com/rulixiang/afa

Accelerating DETR Convergence via Semantic-Aligned Matching

• Paper: https://arxiv.org/abs/2203.06883
• Code: https://github.com/ZhangGongjie/SAM-DETR

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

• Paper: https://arxiv.org/abs/2203.01305
• Code: https://github.com/FengLi-ust/DN-DETR
• 中文解读: https://mp.weixin.qq.com/s/xdMfZ_L628Ru1d1iaMny0w

Style Transformer for Image Inversion and Editing

• Paper: https://arxiv.org/abs/2203.07932
• Code: https://github.com/sapphire497/style-transformer

MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer

• Paper: https://arxiv.org/abs/2203.10981

• Code: https://github.com/kuanchihhuang/MonoDTR

Mask Transfiner for High-Quality Instance Segmentation

• Paper: https://arxiv.org/abs/2111.13673
• Code: https://github.com/SysCV/transfiner

Language as Queries for Referring Video Object Segmentation

• Paper: https://arxiv.org/abs/2201.00487
• Code: https://github.com/wjn922/ReferFormer
• 中文解读：https://mp.weixin.qq.com/s/MkQT8QWSYoYVhJ1RSF6oPQ

X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning

• Paper: https://arxiv.org/abs/2203.00843
• Code: https://github.com/CurryYuan/X-Trans2Cap

AdaMixer: A Fast-Converging Query-Based Object Detector

• Paper(Oral): https://arxiv.org/abs/2203.16507
• Code: https://github.com/MCG-NJU/AdaMixer

Omni-DETR: Omni-Supervised Object Detection with Transformers

• Paper: https://arxiv.org/abs/2203.16089
• Code: https://github.com/amazon-research/omni-detr

SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition

• Paper: https://arxiv.org/abs/2203.10209

• Code: https://github.com/mxin262/SwinTextSpotter

TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting

• Paper(Oral): https://arxiv.org/abs/2204.01018
• Code: https://github.com/SvipRepetitionCounting/TransRAC

Collaborative Transformers for Grounded Situation Recognition

• Paper: https://arxiv.org/abs/2203.16518
• Code: https://github.com/jhcho99/CoFormer

NFormer: Robust Person Re-identification with Neighbor Transformer

• Paper: https://arxiv.org/abs/2204.09331
• Code: https://github.com/haochenheheda/NFormer

Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation

• Paper: https://arxiv.org/abs/2201.06889
• Code: None

Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer

• Paper(Oral): https://arxiv.org/abs/2204.08680
• Code: https://github.com/zengwang430521/TCFormer

A New Dataset and Transformer for Stereoscopic Video Super-Resolution

• Paper: https://arxiv.org/abs/2204.10039
• Code: https://github.com/H-deep/Trans-SVSR/
• Dataset: http://shorturl.at/mpwGX

Safe Self-Refinement for Transformer-based Domain Adaptation

• Paper: https://arxiv.org/abs/2204.07683
• Code: https://github.com/tsun/SSRT

Fast Point Transformer

• Homepage: http://cvlab.postech.ac.kr/research/FPT/
• Paper: https://arxiv.org/abs/2112.04702
• Code: https://github.com/POSTECH-CVLab/FastPointTransformer

Transformer Decoders with MultiModal Regularization for Cross-Modal Food Retrieval

• Paper: https://arxiv.org/abs/2204.09730
• Code: https://github.com/mshukor/TFood

DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

• Paper: https://arxiv.org/abs/2111.14887
• Code: https://github.com/lhoyer/DAFormer

Stratified Transformer for 3D Point Cloud Segmentation

• Paper: https://arxiv.org/pdf/2203.14508.pdf
• Code: https://github.com/dvlab-research/Stratified-Transformer

视觉和语言(Vision-Language)

Conditional Prompt Learning for Vision-Language Models

• Paper: https://arxiv.org/abs/2203.05557
• Code: https://github.com/KaiyangZhou/CoOp

Bridging Video-text Retrieval with Multiple Choice Question

• Paper: https://arxiv.org/abs/2201.04850
• Code: https://github.com/TencentARC/MCQ

Visual Abductive Reasoning

• Paper: https://arxiv.org/abs/2203.14040
• Code: https://github.com/leonnnop/VAR

自监督学习(Self-supervised Learning)

UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

• Paper: https://arxiv.org/abs/2203.06965
• Code: None

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

HCSC: Hierarchical Contrastive Selective Coding

• Homepage: https://github.com/gyfastas/HCSC
• Paper: https://arxiv.org/abs/2202.00455
• 中文解读: https://mp.weixin.qq.com/s/jkYR8mYp-e645qk8kfPNKQ

DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis

• Paper: https://arxiv.org/abs/2204.10437

• Code: https://github.com/JLiangLab/DiRA

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

AlignMixup: Improving Representations By Interpolating Aligned Features

• Paper: https://arxiv.org/abs/2103.15375
• Code: https://github.com/shashankvkt/AlignMixup_CVPR22

知识蒸馏(Knowledge Distillation)

Decoupled Knowledge Distillation

• Paper: https://arxiv.org/abs/2203.08679
• Code: https://github.com/megvii-research/mdistiller
• 中文解读：https://mp.weixin.qq.com/s/-4AA0zKIXh9Ei9-vc5jOhw

目标检测(Object Detection)

BoxeR: Box-Attention for 2D and 3D Transformers

• Paper: https://arxiv.org/abs/2111.13087
• Code: https://github.com/kienduynguyen/BoxeR
• 中文解读：https://mp.weixin.qq.com/s/UnUJJBwcAsRgz6TnQf_b7w

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

• Paper: https://arxiv.org/abs/2203.01305
• Code: https://github.com/FengLi-ust/DN-DETR
• 中文解读: https://mp.weixin.qq.com/s/xdMfZ_L628Ru1d1iaMny0w

Accelerating DETR Convergence via Semantic-Aligned Matching

• Paper: https://arxiv.org/abs/2203.06883
• Code: https://github.com/ZhangGongjie/SAM-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

A Dual Weighting Label Assignment Scheme for Object Detection

• Paper: https://arxiv.org/abs/2203.09730
• Code: https://github.com/strongwolf/DW

AdaMixer: A Fast-Converging Query-Based Object Detector

• Paper(Oral): https://arxiv.org/abs/2203.16507
• Code: https://github.com/MCG-NJU/AdaMixer

Omni-DETR: Omni-Supervised Object Detection with Transformers

• Paper: https://arxiv.org/abs/2203.16089
• Code: https://github.com/amazon-research/omni-detr

SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection

• Paper(Oral): https://arxiv.org/abs/2203.06398
• Code: https://github.com/CityU-AIM-Group/SIGMA

半监督目标检测

Dense Learning based Semi-Supervised Object Detection

• Paper: https://arxiv.org/abs/2204.07300

• Code: https://github.com/chenbinghui1/DSL

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

多模态目标跟踪

Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline

• Homepage: https://zhang-pengyu.github.io/DUT-VTUAV/

• Paper: https://arxiv.org/abs/2204.04120

多目标跟踪(Multi-Object Tracking)

Learning of Global Objective for Network Flow in Multi-Object Tracking

• Paper: https://arxiv.org/abs/2203.16210
• Code: None

DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion

• Homepage: https://dancetrack.github.io
• Paper: https://arxiv.org/abs/2111.14690
• Dataset: https://github.com/DanceTrack/DanceTrack

语义分割(Semantic Segmentation)

Novel Class Discovery in Semantic Segmentation

• Homepage: https://ncdss.github.io/
• Paper: https://arxiv.org/abs/2112.01900
• Code: https://github.com/HeliosZhao/NCDSS

Deep Hierarchical Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.14335
• Code: https://github.com/0liliulei/HieraSeg

Rethinking Semantic Segmentation: A Prototype View

• Paper(Oral): https://arxiv.org/abs/2203.15102
• Code: https://github.com/tfzhou/ProtoSeg

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

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers

• Paper: https://arxiv.org/abs/2203.02664
• Code: https://github.com/rulixiang/afa

CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.02668
• Code: https://github.com/CVI-SZU/CLIMS

CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.13505
• Code: https://github.com/CVI-SZU/CCAM

FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation

• Homeapage: http://cvlab.postech.ac.kr/research/FIFO/
• Paper(Oral): https://arxiv.org/abs/2204.01587
• Code: https://github.com/sohyun-l/FIFO

Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.09653
• Code: https://github.com/maeve07/RCA.git

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

Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

• Homepage: https://haochen-wang409.github.io/U2PL/
• Paper: https://arxiv.org/abs/2203.03884
• Code: https://github.com/Haochen-Wang409/U2PL
• 中文解读: https://mp.weixin.qq.com/s/-08olqE7np8A1XQzt6HAgQ

Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

• Paper: https://arxiv.org/pdf/2111.12903.pdf
• Code: https://github.com/yyliu01/PS-MT

域自适应语义分割

Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation

• Paper: https://arxiv.org/abs/2111.12940
• Code: https://github.com/BIT-DA/RIPU

DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

• Paper: https://arxiv.org/abs/2111.14887
• Code: https://github.com/lhoyer/DAFormer

无监督语义分割

GroupViT: Semantic Segmentation Emerges from Text Supervision

• Homepage: https://jerryxu.net/GroupViT/
• Paper: https://arxiv.org/abs/2202.11094
• Demo: https://youtu.be/DtJsWIUTW-Y

少样本语义分割

Generalized Few-shot Semantic Segmentation

• Paper: https://jiaya.me/papers/cvpr22_zhuotao.pdf
• Code: https://github.com/dvlab-research/GFS-Seg

实例分割(Instance Segmentation)

BoxeR: Box-Attention for 2D and 3D Transformers

• Paper: https://arxiv.org/abs/2111.13087
• Code: https://github.com/kienduynguyen/BoxeR
• 中文解读：https://mp.weixin.qq.com/s/UnUJJBwcAsRgz6TnQf_b7w

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

Mask Transfiner for High-Quality Instance Segmentation

• Paper: https://arxiv.org/abs/2111.13673
• Code: https://github.com/SysCV/transfiner

Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity

• Homepage: https://sites.google.com/view/generic-grouping/

• Paper: https://arxiv.org/abs/2204.06107

• Code: https://github.com/facebookresearch/Generic-Grouping

自监督实例分割

FreeSOLO: Learning to Segment Objects without Annotations

• Paper: https://arxiv.org/abs/2202.12181
• Code: https://github.com/NVlabs/FreeSOLO

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

Temporally Efficient Vision Transformer for Video Instance Segmentation

• Paper: https://arxiv.org/abs/2204.08412
• Code: https://github.com/hustvl/TeViT

全景分割(Panoptic Segmentation)

Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers

• Paper: https://arxiv.org/abs/2109.03814
• Code: https://github.com/zhiqi-li/Panoptic-SegFormer

Large-scale Video Panoptic Segmentation in the Wild: A Benchmark

• Paper: https://github.com/VIPSeg-Dataset/VIPSeg-Dataset/blob/main/VIPSeg2022.pdf
• Code: https://github.com/VIPSeg-Dataset/VIPSeg-Dataset
• Dataset: https://github.com/VIPSeg-Dataset/VIPSeg-Dataset

小样本分类(Few-Shot Classification)

Integrative Few-Shot Learning for Classification and Segmentation

• Paper: https://arxiv.org/abs/2203.15712
• Code: https://github.com/dahyun-kang/ifsl

Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification

• Paper: https://arxiv.org/abs/2106.05517
• Code: https://github.com/LouieYang/MCL

小样本分割(Few-Shot Segmentation)

Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

• Paper: https://arxiv.org/abs/2203.07615
• Code: https://github.com/chunbolang/BAM

Integrative Few-Shot Learning for Classification and Segmentation

• Paper: https://arxiv.org/abs/2203.15712
• Code: https://github.com/dahyun-kang/ifsl

Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation

• Paper: https://arxiv.org/abs/2204.10638
• Code: None

图像抠图(Image Matting)

Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation

• Paper: https://arxiv.org/abs/2201.06889
• Code: None

视频理解(Video Understanding)

Self-supervised Video Transformer

• Homepage: https://kahnchana.github.io/svt/
• Paper: https://arxiv.org/abs/2112.01514
• Code: https://github.com/kahnchana/svt

TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting

• Paper(Oral): https://arxiv.org/abs/2204.01018
• Code: https://github.com/SvipRepetitionCounting/TransRAC

FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment

• Paper(Oral): https://arxiv.org/abs/2204.03646

• Dataset: https://github.com/xujinglin/FineDiving

• Code: https://github.com/xujinglin/FineDiving

• 中文解读：https://mp.weixin.qq.com/s/8t12Y34eMNwvJr8PeryWXg

Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition

• Paper(Oral): https://arxiv.org/abs/2204.02148
• Code: None

行为识别(Action Recognition)

Spatio-temporal Relation Modeling for Few-shot Action Recognition

• Paper: https://arxiv.org/abs/2112.05132
• Code: https://github.com/Anirudh257/strm

动作检测(Action Detection)

End-to-End Semi-Supervised Learning for Video Action Detection

• Paper: https://arxiv.org/abs/2203.04251
• Code: None

图像编辑(Image Editing)

Style Transformer for Image Inversion and Editing

• Paper: https://arxiv.org/abs/2203.07932
• Code: https://github.com/sapphire497/style-transformer

Blended Diffusion for Text-driven Editing of Natural Images

• Paper: https://arxiv.org/abs/2111.14818
• Code: https://github.com/omriav/blended-diffusion

SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing

• Homepage: https://semanticstylegan.github.io/

• Paper: https://arxiv.org/abs/2112.02236

• Demo: https://semanticstylegan.github.io/videos/demo.mp4

Low-level Vision

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

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

Restormer: Efficient Transformer for High-Resolution Image Restoration

• Paper: https://arxiv.org/abs/2111.09881
• Code: https://github.com/swz30/Restormer

Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements

• Paper(Oral): https://arxiv.org/abs/2111.12855
• Code: https://github.com/edongdongchen/REI

超分辨率(Super-Resolution)

图像超分辨率(Image Super-Resolution)

Learning the Degradation Distribution for Blind Image Super-Resolution

• Paper: https://arxiv.org/abs/2203.04962
• Code: https://github.com/greatlog/UnpairedSR

视频超分辨率(Video Super-Resolution)

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

Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling

• Paper: https://arxiv.org/abs/2204.07114
• Code: None

A New Dataset and Transformer for Stereoscopic Video Super-Resolution

• Paper: https://arxiv.org/abs/2204.10039
• Code: https://github.com/H-deep/Trans-SVSR/
• Dataset: http://shorturl.at/mpwGX

去模糊(Deblur)

图像去模糊(Image Deblur)

Learning to Deblur using Light Field Generated and Real Defocus Images

• Homepage: http://lyruan.com/Projects/DRBNet/

• Paper(Oral): https://arxiv.org/abs/2204.00442

• Code: https://github.com/lingyanruan/DRBNet

3D点云(3D Point Cloud)

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

• Homepage: https://point-bert.ivg-research.xyz/

• Paper: https://arxiv.org/abs/2111.14819

• Code: https://github.com/lulutang0608/Point-BERT

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

Fast Point Transformer

• Homepage: http://cvlab.postech.ac.kr/research/FPT/
• Paper: https://arxiv.org/abs/2112.04702
• Code: https://github.com/POSTECH-CVLab/FastPointTransformer

RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds

• Paper: https://arxiv.org/abs/2205.11028
• Code: https://github.com/gxd1994/RCP

The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant Learning via Pose-aware Convolution

• Paper: https://arxiv.org/abs/2205.15210
• Code: https://github.com/GostInShell/PaRI-Conv

3D目标检测(3D Object Detection)

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds

• Paper(Oral): https://arxiv.org/abs/2203.11139

• Code: https://github.com/yifanzhang713/IA-SSD

• Demo: https://www.youtube.com/watch?v=3jP2o9KXunA

BoxeR: Box-Attention for 2D and 3D Transformers

• Paper: https://arxiv.org/abs/2111.13087
• Code: https://github.com/kienduynguyen/BoxeR
• 中文解读：https://mp.weixin.qq.com/s/UnUJJBwcAsRgz6TnQf_b7w

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

MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer

• Paper: https://arxiv.org/abs/2203.10981
• Code: https://github.com/kuanchihhuang/MonoDTR

HyperDet3D: Learning a Scene-conditioned 3D Object Detector

• Paper: https://arxiv.org/abs/2204.05599
• Code: None

OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data

• Paper: https://arxiv.org/abs/2204.06577
• Code: https://github.com/dschinagl/occam

DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection

• Homepage: https://thudair.baai.ac.cn/index
• Paper: https://arxiv.org/abs/2204.05575
• Code: https://github.com/AIR-THU/DAIR-V2X

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

• Homepage: https://ithaca365.mae.cornell.edu/

• Paper: https://arxiv.org/abs/2208.01166

3D语义分割(3D Semantic Segmentation)

Scribble-Supervised LiDAR Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.08537
• Dataset: https://github.com/ouenal/scribblekitti

Stratified Transformer for 3D Point Cloud Segmentation

• Paper: https://arxiv.org/pdf/2203.14508.pdf
• Code: https://github.com/dvlab-research/Stratified-Transformer

3D实例分割(3D Instance Segmentation)

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

• Homepage: https://ithaca365.mae.cornell.edu/

• Paper: https://arxiv.org/abs/2208.01166

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

PTTR: Relational 3D Point Cloud Object Tracking with Transformer

• Paper: https://arxiv.org/abs/2112.02857
• Code: https://github.com/Jasonkks/PTTR

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

Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation

• Paper: https://arxiv.org/abs/2203.07697
• Code: None
• 中文解读：https://mp.weixin.qq.com/s/L_F28IFLXvs5R4V9TTUpRw

BEV: Putting People in their Place: Monocular Regression of 3D People in Depth

• Homepage: https://arthur151.github.io/BEV/BEV.html
• Paper: https://arxiv.org/abs/2112.08274
• Code: https://github.com/Arthur151/ROMP
• Dataset: https://github.com/Arthur151/Relative_Human
• Demo: https://www.youtube.com/watch?v=Q62fj_6AxRI

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

行人重识别(Person Re-identification)

NFormer: Robust Person Re-identification with Neighbor Transformer

• Paper: https://arxiv.org/abs/2204.09331
• Code: https://github.com/haochenheheda/NFormer

伪装物体检测(Camouflaged Object Detection)

Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection

• Paper: https://arxiv.org/abs/2203.02688
• Code: https://github.com/lartpang/ZoomNet

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

P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior

• Paper: https://arxiv.org/abs/2204.02091
• Code: https://github.com/SysCV/P3Depth

Multi-Frame Self-Supervised Depth with Transformers

• Homepage: https://sites.google.com/tri.global/depthformer

• Paper: https://arxiv.org/abs/2204.07616

• Code: None

立体匹配(Stereo Matching)

ACVNet: Attention Concatenation Volume for Accurate and Efficient Stereo Matching

• Paper: https://arxiv.org/abs/2203.02146
• Code: https://github.com/gangweiX/ACVNet

特征匹配(Feature Matching)

ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching

• Paper: https://arxiv.org/abs/2204.11700
• 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

A Keypoint-based Global Association Network for Lane Detection

• Paper: https://arxiv.org/abs/2204.07335
• Code: https://github.com/Wolfwjs/GANet

光流估计(Optical Flow Estimation)

Imposing Consistency for Optical Flow Estimation

• Paper: https://arxiv.org/abs/2204.07262
• Code: None

Deep Equilibrium Optical Flow Estimation

• Paper: https://arxiv.org/abs/2204.08442
• Code: https://github.com/locuslab/deq-flow

GMFlow: Learning Optical Flow via Global Matching

• Paper(Oral): https://arxiv.org/abs/2111.13680
• Code: https://github.com/haofeixu/gmflow

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

图像检索(Image Retrieval)

Correlation Verification for Image Retrieval

• Paper(Oral): https://arxiv.org/abs/2204.01458
• Code: https://github.com/sungonce/CVNet

人脸识别(Face Recognition)

AdaFace: Quality Adaptive Margin for Face Recognition

• Paper(Oral): https://arxiv.org/abs/2204.00964
• Code: https://github.com/mk-minchul/AdaFace

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

Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

• Paper: https://arxiv.org/abs/2111.12918
• Code: https://github.com/FBLADL/ACPL

DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis

• Paper: https://arxiv.org/abs/2204.10437

• Code: https://github.com/JLiangLab/DiRA

视频生成(Video Generation)

StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2

• Homepage: https://universome.github.io/stylegan-v

• Paper: https://arxiv.org/abs/2112.14683

• Code: https://github.com/universome/stylegan-v

• Demo: https://kaust-cair.s3.amazonaws.com/stylegan-v/stylegan-v.mp4

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

参考视频目标分割(Referring Video Object Segmentation)

Language as Queries for Referring Video Object Segmentation

• Paper: https://arxiv.org/abs/2201.00487
• Code: https://github.com/wjn922/ReferFormer

ReSTR: Convolution-free Referring Image Segmentation Using Transformers

• Paper: https://arxiv.org/abs/2203.16768
• Code: None

步态识别(Gait Recognition)

Gait Recognition in the Wild with Dense 3D Representations and A Benchmark

• Homepage: https://gait3d.github.io/
• Paper: https://arxiv.org/abs/2204.02569
• Code: https://github.com/Gait3D/Gait3D-Benchmark

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

异常检测(Anomaly Detection)

UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection

• Paper: https://arxiv.org/abs/2111.08644

• Dataset: https://github.com/lilygeorgescu/UBnormal

Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection

• Paper(Oral): https://arxiv.org/abs/2111.09099
• Code: https://github.com/ristea/sspcab

对抗样本)

对抗样本(Adversarial Examples)

Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

• Paper: https://arxiv.org/abs/2203.03818
• Code: https://github.com/hncszyq/ShadowAttack

LAS-AT: Adversarial Training with Learnable Attack Strategy

• Paper(Oral): https://arxiv.org/abs/2203.06616
• Code: https://github.com/jiaxiaojunQAQ/LAS-AT

Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection

• Paper: https://arxiv.org/abs/2112.04532
• Code: https://github.com/joellliu/SegmentAndComplete

弱监督物体检测(Weakly Supervised Object Localization)

Weakly Supervised Object Localization as Domain Adaption

• Paper: https://arxiv.org/abs/2203.01714
• Code: https://github.com/zh460045050/DA-WSOL_CVPR2022

雷达目标检测(Radar Object Detection)

Exploiting Temporal Relations on Radar Perception for Autonomous Driving

• Paper: https://arxiv.org/abs/2204.01184
• Code: None

高光谱图像重建(Hyperspectral Image Reconstruction)

Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction

• Paper: https://arxiv.org/abs/2111.07910
• Code: https://github.com/caiyuanhao1998/MST

图像拼接(Image Stitching)

Deep Rectangling for Image Stitching: A Learning Baseline

• Paper(Oral): https://arxiv.org/abs/2203.03831

• Code: https://github.com/nie-lang/DeepRectangling

• Dataset: https://github.com/nie-lang/DeepRectangling

• 中文解读：https://mp.weixin.qq.com/s/lp5AnrtO_9urp-Fv6Z0l2Q

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

Action Counting

TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting

• Paper(Oral): https://arxiv.org/abs/2204.01018
• Dataset: https://svip-lab.github.io/dataset/RepCount_dataset.html
• Code: https://github.com/SvipRepetitionCounting/TransRAC

Grounded Situation Recognition

Collaborative Transformers for Grounded Situation Recognition

• Paper: https://arxiv.org/abs/2203.16518
• Code: https://github.com/jhcho99/CoFormer

Zero-shot Learning

Unseen Classes at a Later Time? No Problem

• Paper: https://arxiv.org/abs/2203.16517
• Code: https://github.com/sumitramalagi/Unseen-classes-at-a-later-time

DeepFakes

Detecting Deepfakes with Self-Blended Images

• Paper(Oral): https://arxiv.org/abs/2204.08376

• Code: https://github.com/mapooon/SelfBlendedImages

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
• 中文解读：https://mp.weixin.qq.com/s/mJ8HzY6C0GifxsErJIS3Mg

Scribble-Supervised LiDAR Semantic Segmentation

• Paper: https://arxiv.org/abs/2203.08537
• Dataset: https://github.com/ouenal/scribblekitti

Deep Rectangling for Image Stitching: A Learning Baseline

• Paper(Oral): https://arxiv.org/abs/2203.03831
• Code: https://github.com/nie-lang/DeepRectangling
• Dataset: https://github.com/nie-lang/DeepRectangling
• 中文解读：https://mp.weixin.qq.com/s/lp5AnrtO_9urp-Fv6Z0l2Q

ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer

• Homepage: https://ai.stanford.edu/~rhgao/objectfolder2.0/
• Paper: https://arxiv.org/abs/2204.02389
• Dataset: https://github.com/rhgao/ObjectFolder
• Demo：https://youtu.be/e5aToT3LkRA

Shape from Polarization for Complex Scenes in the Wild

• Homepage: https://chenyanglei.github.io/sfpwild/index.html
• Paper: https://arxiv.org/abs/2112.11377
• Code: https://github.com/ChenyangLEI/sfp-wild

Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline

• Homepage: https://zhang-pengyu.github.io/DUT-VTUAV/
• Paper: https://arxiv.org/abs/2204.04120

TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting

• Paper(Oral): https://arxiv.org/abs/2204.01018
• Dataset: https://svip-lab.github.io/dataset/RepCount_dataset.html
• Code: https://github.com/SvipRepetitionCounting/TransRAC

FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment

• Paper(Oral): https://arxiv.org/abs/2204.03646
• Dataset: https://github.com/xujinglin/FineDiving
• Code: https://github.com/xujinglin/FineDiving
• 中文解读：https://mp.weixin.qq.com/s/8t12Y34eMNwvJr8PeryWXg

Aesthetic Text Logo Synthesis via Content-aware Layout Inferring

• Paper: https://arxiv.org/abs/2204.02701
• Dataset: https://github.com/yizhiwang96/TextLogoLayout
• Code: https://github.com/yizhiwang96/TextLogoLayout

DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection

• Homepage: https://thudair.baai.ac.cn/index
• Paper: https://arxiv.org/abs/2204.05575
• Code: https://github.com/AIR-THU/DAIR-V2X

A New Dataset and Transformer for Stereoscopic Video Super-Resolution

• Paper: https://arxiv.org/abs/2204.10039
• Code: https://github.com/H-deep/Trans-SVSR/
• Dataset: http://shorturl.at/mpwGX

Putting People in their Place: Monocular Regression of 3D People in Depth

• Homepage: https://arthur151.github.io/BEV/BEV.html

• Paper: https://arxiv.org/abs/2112.08274

• Code:https://github.com/Arthur151/ROMP

• Dataset: https://github.com/Arthur151/Relative_Human

UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection

• Paper: https://arxiv.org/abs/2111.08644
• Dataset: https://github.com/lilygeorgescu/UBnormal

DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion

• Homepage: https://dancetrack.github.io
• Paper: https://arxiv.org/abs/2111.14690
• Dataset: https://github.com/DanceTrack/DanceTrack

Visual Abductive Reasoning

• Paper: https://arxiv.org/abs/2203.14040
• Code: https://github.com/leonnnop/VAR

Large-scale Video Panoptic Segmentation in the Wild: A Benchmark

• Paper: https://github.com/VIPSeg-Dataset/VIPSeg-Dataset/blob/main/VIPSeg2022.pdf
• Code: https://github.com/VIPSeg-Dataset/VIPSeg-Dataset
• Dataset: https://github.com/VIPSeg-Dataset/VIPSeg-Dataset

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

• Homepage: https://ithaca365.mae.cornell.edu/

• Paper: https://arxiv.org/abs/2208.01166

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

Splicing ViT Features for Semantic Appearance Transfer

• Homepage: https://splice-vit.github.io/
• Paper: https://arxiv.org/abs/2201.00424
• Code: https://github.com/omerbt/Splice

Visual Abductive Reasoning

• Paper: https://arxiv.org/abs/2203.14040
• Code: https://github.com/leonnnop/VAR

其他(Others)

Kubric: A scalable dataset generator

• Paper: https://arxiv.org/abs/2203.03570
• Code: https://github.com/google-research/kubric
• 中文解读：https://mp.weixin.qq.com/s/mJ8HzY6C0GifxsErJIS3Mg

X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning

• Paper: https://arxiv.org/abs/2203.00843
• Code: https://github.com/CurryYuan/X-Trans2Cap

Balanced MSE for Imbalanced Visual Regression

• Paper(Oral): https://arxiv.org/abs/2203.16427
• Code: https://github.com/jiawei-ren/BalancedMSE

SNUG: Self-Supervised Neural Dynamic Garments

• Homepage: http://mslab.es/projects/SNUG/
• Paper(Oral): https://arxiv.org/abs/2204.02219
• Code: https://github.com/isantesteban/snug

Shape from Polarization for Complex Scenes in the Wild

• Homepage: https://chenyanglei.github.io/sfpwild/index.html
• Paper: https://arxiv.org/abs/2112.11377
• Code: https://github.com/ChenyangLEI/sfp-wild

LASER: LAtent SpacE Rendering for 2D Visual Localization

• Paper(Oral): https://arxiv.org/abs/2204.00157
• Code: None

Single-Photon Structured Light

• Paper(Oral): https://arxiv.org/abs/2204.05300
• Code: None

3DeformRS: Certifying Spatial Deformations on Point Clouds

• Paper: https://arxiv.org/abs/2204.05687
• Code: None

Aesthetic Text Logo Synthesis via Content-aware Layout Inferring

• Paper: https://arxiv.org/abs/2204.02701
• Dataset: https://github.com/yizhiwang96/TextLogoLayout
• Code: https://github.com/yizhiwang96/TextLogoLayout

Self-Supervised Predictive Learning: A Negative-Free Method for Sound Source Localization in Visual Scenes

• Paper: https://arxiv.org/abs/2203.13412
• Code: https://github.com/zjsong/SSPL

Robust and Accurate Superquadric Recovery: a Probabilistic Approach

• Paper(Oral): https://arxiv.org/abs/2111.14517
• Code: https://github.com/bmlklwx/EMS-superquadric_fitting

Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence

• Paper: https://arxiv.org/abs/2203.00911
• Code: None

Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer

• Paper(Oral): https://arxiv.org/abs/2204.08680
• Code: https://github.com/zengwang430521/TCFormer

DeepDPM: Deep Clustering With an Unknown Number of Clusters

• Paper: https://arxiv.org/abs/2203.14309
• Code: https://github.com/BGU-CS-VIL/DeepDPM

ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

• Paper: https://arxiv.org/abs/2111.14447
• Code: https://github.com/YoadTew/zero-shot-image-to-text

Proto2Proto: Can you recognize the car, the way I do?

• Paper: https://arxiv.org/abs/2204.11830
• Code: https://github.com/archmaester/proto2proto

Putting People in their Place: Monocular Regression of 3D People in Depth

• Homepage: https://arthur151.github.io/BEV/BEV.html
• Paper: https://arxiv.org/abs/2112.08274
• Code:https://github.com/Arthur151/ROMP
• Dataset: https://github.com/Arthur151/Relative_Human

Light Field Neural Rendering

• Homepage: https://light-field-neural-rendering.github.io/
• Paper(Oral): https://arxiv.org/abs/2112.09687
• Code: https://github.com/google-research/google-research/tree/master/light_field_neural_rendering

Neural Texture Extraction and Distribution for Controllable Person Image Synthesis

• Paper: https://arxiv.org/abs/2204.06160
• Code: https://github.com/RenYurui/Neural-Texture-Extraction-Distribution

Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning

• Paper: https://arxiv.org/abs/2203.14333
• Code: https://github.com/0liliulei/LIIR