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CVPR2020 論文とオープンソースプロジェクト集

CVPR 2021 論文・オープンソースプロジェクト集(Papers with Code)

CVPR 2021 論文・オープンソースプロジェクト集(papers with code)！

CVPR 2021 コレクション一覧：http://cvpr2021.thecvf.com/sites/default/files/2021-03/accepted_paper_ids.txt

注1：CVPR2021論文やオープンソースプロジェクトの課題と共有の投稿をお待ちしています!

注2：关于往年CV顶会論文以及その他优质CV論文和大盘点，详见： https://github.com/amusi/daily-paper-computer-vision

CVPR 2021 中奖群已成立！已经收录的同学，可以添加微信：CVer9999，请备注：CVPR2021已收录+姓名+学校/公司名称！一定要根据格式申请，可以拉你进群沟通开会等事宜。

【CVPR 2021 論文开源目录】

• Backbone
• NAS
• GAN
• VAE
• Visual Transformer
• Regularization
• SLAM
• ロングテール(Long-Tailed)
• データ拡張(Data Augmentation)
• 教師なし/自己教師(Self-Supervised)
• 半监督(Semi-Supervised)
• カプセルネットワーク(Capsule Network)
• 2D物体検出(Object Detection)
• 单/多物体追跡(Object Tracking)
• セマンティックセグメンテーション(Semantic Segmentation)
• インスタンスセグメンテーション(Instance Segmentation)
• 全景分割(Panoptic Segmentation)
• 医学画像分割(Medical Image Segmentation)
• 動画物体分割(Video-Object-Segmentation)
• 交互式動画物体分割(Interactive-Video-Object-Segmentation)
• 显著性検出(Saliency Detection)
• 伪装物体検出(Camouflaged Object Detection)
• 协同显著性検出(Co-Salient Object Detection)
• 画像抠图(Image Matting)
• 歩行者重識別(Person Re-identification)
• 歩行者搜索(Person Search)
• 動画理解/行動識別(Video Understanding)
• 顔識別(Face Recognition)
• 顔検出(Face Detection)
• Face Anti-spoofing(Face Anti-Spoofing)
• Deepfake検出(Deepfake Detection)
• 顔年龄推定(Age-Estimation)
• 顔表情識別(Facial-Expression-Recognition)
• Deepfakes
• 人体解析(Human Parsing)
• 2D/3D人体姿勢推定(2D/3D Human Pose Estimation)
• 动物姿勢推定(Animal Pose Estimation)
• Human Volumetric Capture
• シーンテキスト識別(Scene Text Recognition)
• 画像圧縮(Image Compression)
• モデル圧縮/枝刈り/量化
• 知识蒸馏(Knowledge Distillation)
• 超解像(Super-Resolution)
• 去雾(Dehazing)
• 画像恢复(Image Restoration)
• 画像补全(Image Inpainting)
• 画像编辑(Image Editing)
• 画像匹配(Image Matching)
• 画像融合(Image Blending)
• 反光去除(Reflection Removal)
• 3D点群分類(3D Point Clouds Classification)
• 3D物体検出(3D Object Detection)
• 3Dセマンティックセグメンテーション(3D Semantic Segmentation)
• 3D全景分割(3D Panoptic Segmentation)
• 3D物体追跡(3D Object Tracking)
• 3D点群レジストレーション(3D Point Cloud Registration)
• 3D点群补全(3D-Point-Cloud-Completion)
• 3D重建(3D Reconstruction)
• 6D位姿推定(6D Pose Estimation)
• 相机姿勢推定(Camera Pose Estimation)
• 深度推定(Depth Estimation)
• [立体匹配(Stereo Matching)](#Stereo Matching)
• 光流推定(Flow Estimation)
• 軌跡予測(Trajectory Prediction)
• 对抗样本(Adversarial-Examples)
• 画像检索(Image Retrieval)
• 動画检索(Video Retrieval)
• 跨模态检索(Cross-modal Retrieval)
• Zero-Shot Learning
• 联邦学习(Federated Learning)
• 動画插帧(Video Frame Interpolation)
• 视觉推理(Visual Reasoning)
• 视图合成(Visual Synthesis)
• スタイル変換(Style Transfer)
• 布局生成(Layout Generation)
• Domain Generalization
• Domain Adaptation
• Open-Set Recognition
• Adversarial Attack
• Human-Object Interaction (HOI)検出
• 阴影去除(Shadow Removal)
• 虚拟试衣
• データセット(Datasets)
• その他(Others)
• 待添加(TODO)
• 採択されたか不明(Not Sure)

Backbone

Lite-HRNet: A Lightweight High-Resolution Network

• Paper: https://arxiv.org/abs/2104.06403
• https://github.com/HRNet/Lite-HRNet

CondenseNet V2: Sparse Feature Reactivation for Deep Networks

• Paper: https://arxiv.org/abs/2104.04382

• Code: https://github.com/jianghaojun/CondenseNetV2

Diverse Branch Block: Building a Convolution as an Inception-like Unit

• Paper: https://arxiv.org/abs/2103.13425

• Code: https://github.com/DingXiaoH/DiverseBranchBlock

Scaling Local Self-Attention For Parameter Efficient Visual Backbones

• Paper(Oral): https://arxiv.org/abs/2103.12731

• Code: None

ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network

• Paper: https://arxiv.org/abs/2007.00992
• Code: https://github.com/clovaai/rexnet

Involution: Inverting the Inherence of Convolution for Visual Recognition

• Paper: https://github.com/d-li14/involution
• Code: https://arxiv.org/abs/2103.06255

Coordinate Attention for Efficient Mobile Network Design

• Paper: https://arxiv.org/abs/2103.02907
• Code: https://github.com/Andrew-Qibin/CoordAttention

Inception Convolution with Efficient Dilation Search

• Paper: https://arxiv.org/abs/2012.13587
• Code: https://github.com/yifan123/IC-Conv

RepVGG: Making VGG-style ConvNets Great Again

• Paper: https://arxiv.org/abs/2101.03697
• Code: https://github.com/DingXiaoH/RepVGG

NAS

Combined Depth Space based Architecture Search For Person Re-identification

• Paper: https://arxiv.org/abs/2104.04163
• Code: None

DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation

• Paper(Oral): https://arxiv.org/abs/2103.15954
• Code: None

HR-NAS: Searching Efficient High-Resolution Neural Architectures with Transformers

• Paper(Oral): None
• Code: https://github.com/dingmyu/HR-NAS

Neural Architecture Search with Random Labels

• Paper: https://arxiv.org/abs/2101.11834
• Code: None

Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search

• Paper: https://arxiv.org/abs/2101.11342
• Code: None

Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation

• Paper: None
• Code: None

Prioritized Architecture Sampling with Monto-Carlo Tree Search

• Paper: https://arxiv.org/abs/2103.11922
• Code: https://github.com/xiusu/NAS-Bench-Macro

Contrastive Neural Architecture Search with Neural Architecture Comparators

• Paper: https://arxiv.org/abs/2103.05471
• Code: https://github.com/chenyaofo/CTNAS

AttentiveNAS: Improving Neural Architecture Search via Attentive

• Paper: https://arxiv.org/abs/2011.09011
• Code: None

ReNAS: Relativistic Evaluation of Neural Architecture Search

• Paper: https://arxiv.org/abs/1910.01523
• Code: None

HourNAS: Extremely Fast Neural Architecture

• Paper: https://arxiv.org/abs/2005.14446
• Code: None

Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator

• Paper: https://arxiv.org/abs/2103.07289
• Code: https://github.com/eric8607242/SGNAS

OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection

• Paper: https://arxiv.org/abs/2103.04507
• Code: https://github.com/VDIGPKU/OPANAS

Inception Convolution with Efficient Dilation Search

• Paper: https://arxiv.org/abs/2012.13587
• Code: None

GAN

Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer

• Paper: https://arxiv.org/abs/2104.05376
• Code: https://github.com/PaddlePaddle/PaddleGAN/

Regularizing Generative Adversarial Networks under Limited Data

• Homepage: https://hytseng0509.github.io/lecam-gan/
• Paper: https://faculty.ucmerced.edu/mhyang/papers/cvpr2021_gan_limited_data.pdf
• Code: https://github.com/google/lecam-gan

Towards Real-World Blind Face Restoration with Generative Facial Prior

• Paper: https://arxiv.org/abs/2101.04061
• Code: None

TediGAN: Text-Guided Diverse Image Generation and Manipulation

• Homepage: https://xiaweihao.com/projects/tedigan/

• Paper: https://arxiv.org/abs/2012.03308

• Code: https://github.com/weihaox/TediGAN

Generative Hierarchical Features from Synthesizing Image

• Homepage: https://genforce.github.io/ghfeat/

• Paper(Oral): https://arxiv.org/abs/2007.10379

• Code: https://github.com/genforce/ghfeat

Teachers Do More Than Teach: Compressing Image-to-Image Models

• Paper: https://arxiv.org/abs/2103.03467
• Code: https://github.com/snap-research/CAT

HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms

• Paper: https://arxiv.org/abs/2011.11731
• Code: https://github.com/mahmoudnafifi/HistoGAN

pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

• Homepage: https://marcoamonteiro.github.io/pi-GAN-website/

• Paper(Oral): https://arxiv.org/abs/2012.00926

• Code: None

DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

• Paper: https://arxiv.org/abs/2103.07893
• Code: None

Diverse Semantic Image Synthesis via Probability Distribution Modeling

• Paper: https://arxiv.org/abs/2103.06878
• Code: https://github.com/tzt101/INADE.git

LOHO: Latent Optimization of Hairstyles via Orthogonalization

• Paper: https://arxiv.org/abs/2103.03891
• Code: None

PISE: Person Image Synthesis and Editing with Decoupled GAN

• Paper: https://arxiv.org/abs/2103.04023
• Code: https://github.com/Zhangjinso/PISE

DeFLOCNet: Deep Image Editing via Flexible Low-level Controls

• Paper: http://raywzy.com/
• Code: http://raywzy.com/

PD-GAN: Probabilistic Diverse GAN for Image Inpainting

• Paper: http://raywzy.com/
• Code: http://raywzy.com/

Efficient Conditional GAN Transfer with Knowledge Propagation across Classes

• Paper: https://www.researchgate.net/publication/349309756_Efficient_Conditional_GAN_Transfer_with_Knowledge_Propagation_across_Classes
• Code: http://github.com/mshahbazi72/cGANTransfer

Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

• Paper: None
• Code: None

Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

• Paper: https://arxiv.org/abs/2011.14107
• Code: None

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

• Homepage: https://eladrich.github.io/pixel2style2pixel/
• Paper: https://arxiv.org/abs/2008.00951
• Code: https://github.com/eladrich/pixel2style2pixel

A 3D GAN for Improved Large-pose Facial Recognition

• Paper: https://arxiv.org/abs/2012.10545
• Code: None

HumanGAN: A Generative Model of Humans Images

• Paper: https://arxiv.org/abs/2103.06902
• Code: None

ID-Unet: Iterative Soft and Hard Deformation for View Synthesis

• Paper: https://arxiv.org/abs/2103.02264
• Code: https://github.com/MingyuY/Iterative-view-synthesis

CoMoGAN: continuous model-guided image-to-image translation

• Paper(Oral): https://arxiv.org/abs/2103.06879
• Code: https://github.com/cv-rits/CoMoGAN

Training Generative Adversarial Networks in One Stage

• Paper: https://arxiv.org/abs/2103.00430
• Code: None

Closed-Form Factorization of Latent Semantics in GANs

• Homepage: https://genforce.github.io/sefa/
• Paper(Oral): https://arxiv.org/abs/2007.06600
• Code: https://github.com/genforce/sefa

Anycost GANs for Interactive Image Synthesis and Editing

• Paper: https://arxiv.org/abs/2103.03243
• Code: https://github.com/mit-han-lab/anycost-gan

Image-to-image Translation via Hierarchical Style Disentanglement

• Paper: https://arxiv.org/abs/2103.01456
• Code: https://github.com/imlixinyang/HiSD

VAE

Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders

• Homepage: https://taldatech.github.io/soft-intro-vae-web/

• Paper: https://arxiv.org/abs/2012.13253

• Code: https://github.com/taldatech/soft-intro-vae-pytorch

Visual Transformer

Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

• Paper: https://arxiv.org/abs/2104.09224
• Code: https://github.com/autonomousvision/transfuser

Pose Recognition with Cascade Transformers

• Paper: https://arxiv.org/abs/2104.06976

• Code: https://github.com/mlpc-ucsd/PRTR

Variational Transformer Networks for Layout Generation

• Paper: https://arxiv.org/abs/2104.02416
• Code: None

LoFTR: Detector-Free Local Feature Matching with Transformers

• Homepage: https://zju3dv.github.io/loftr/
• Paper: https://arxiv.org/abs/2104.00680
• Code: https://github.com/zju3dv/LoFTR

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

• Paper: https://arxiv.org/abs/2012.15840
• Code: https://github.com/fudan-zvg/SETR

Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers

• Paper: https://arxiv.org/abs/2103.16553
• Code: None

Transformer Tracking

• Paper: https://arxiv.org/abs/2103.15436
• Code: https://github.com/chenxin-dlut/TransT

HR-NAS: Searching Efficient High-Resolution Neural Architectures with Transformers

• Paper(Oral): None
• Code: https://github.com/dingmyu/HR-NAS

MIST: Multiple Instance Spatial Transformer Network

• Paper: https://arxiv.org/abs/1811.10725
• Code: None

Multimodal Motion Prediction with Stacked Transformers

• Paper: https://arxiv.org/abs/2103.11624
• Code: https://decisionforce.github.io/mmTransformer

Revamping cross-modal recipe retrieval with hierarchical Transformers and self-supervised learning

• Paper: https://www.amazon.science/publications/revamping-cross-modal-recipe-retrieval-with-hierarchical-transformers-and-self-supervised-learning

• Code: https://github.com/amzn/image-to-recipe-transformers

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking

• Paper(Oral): https://arxiv.org/abs/2103.11681

• Code: https://github.com/594422814/TransformerTrack

Pre-Trained Image Processing Transformer

• Paper: https://arxiv.org/abs/2012.00364
• Code: None

End-to-End Video Instance Segmentation with Transformers

• Paper(Oral): https://arxiv.org/abs/2011.14503
• Code: https://github.com/Epiphqny/VisTR

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

• Paper(Oral): https://arxiv.org/abs/2011.09094
• Code: https://github.com/dddzg/up-detr

End-to-End Human Object Interaction Detection with HOI Transformer

• Paper: https://arxiv.org/abs/2103.04503
• Code: https://github.com/bbepoch/HoiTransformer

Transformer Interpretability Beyond Attention Visualization

• Paper: https://arxiv.org/abs/2012.09838
• Code: https://github.com/hila-chefer/Transformer-Explainability

Regularization

Regularizing Neural Networks via Adversarial Model Perturbation

• Paper: https://arxiv.org/abs/2010.04925
• Code: https://github.com/hiyouga/AMP-Regularizer

SLAM

Generalizing to the Open World: Deep Visual Odometry with Online Adaptation

• Paper: https://arxiv.org/abs/2103.15279
• Code: https://arxiv.org/abs/2103.15279

ロングテール(Long-Tailed)

Distribution Alignment: A Unified Framework for Long-tail Visual Recognition

• Paper: https://arxiv.org/abs/2103.16370
• Code: https://github.com/Megvii-BaseDetection/DisAlign

Adaptive Class Suppression Loss for Long-Tail Object Detection

• Paper: https://arxiv.org/abs/2104.00885
• Code: https://github.com/CASIA-IVA-Lab/ACSL

Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification

• Paper: https://arxiv.org/abs/2103.14267
• Code: None

データ拡張(Data Augmentation)

Scale-aware Automatic Augmentation for Object Detection

• Paper: https://arxiv.org/abs/2103.17220

• Code: https://github.com/Jia-Research-Lab/SA-AutoAug

教師なし/自己教師(Un/Self-Supervised)

Self-supervised Video Representation Learning by Context and Motion Decoupling

• Paper: https://arxiv.org/abs/2104.00862
• Code: None

Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning

• Homepage: https://fingerrec.github.io/index_files/jinpeng/papers/CVPR2021/project_website.html
• Paper: https://arxiv.org/abs/2009.05769
• Code: https://github.com/FingerRec/BE

Spatially Consistent Representation Learning

• Paper: https://arxiv.org/abs/2103.06122
• Code: None

VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples

• Paper: https://arxiv.org/abs/2103.05905
• Code: https://github.com/tinapan-pt/VideoMoCo

Exploring Simple Siamese Representation Learning

• Paper(Oral): https://arxiv.org/abs/2011.10566
• Code: None

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

• Paper(Oral): https://arxiv.org/abs/2011.09157
• Code: https://github.com/WXinlong/DenseCL

半教師(Semi-Supervised )

Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

• Paper: https://arxiv.org/abs/2103.11402
• Code: None

Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

• Paper: https://arxiv.org/abs/2103.02193
• Code: https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning

カプセルネットワーク(Capsule Network)

Capsule Network is Not More Robust than Convolutional Network

• Paper: https://arxiv.org/abs/2103.15459
• Code: None

2D物体検出(Object Detection)

2D物体検出

IQDet: Instance-wise Quality Distribution Sampling for Object Detection

• Paper: https://arxiv.org/abs/2104.06936
• Code: None

Multi-Scale Aligned Distillation for Low-Resolution Detection

• Paper: https://jiaya.me/papers/ms_align_distill_cvpr21.pdf

• Code: https://github.com/Jia-Research-Lab/MSAD

Adaptive Class Suppression Loss for Long-Tail Object Detection

• Paper: https://arxiv.org/abs/2104.00885
• Code: https://github.com/CASIA-IVA-Lab/ACSL

VarifocalNet: An IoU-aware Dense Object Detector

• Paper(Oral): https://arxiv.org/abs/2008.13367

• Code: https://github.com/hyz-xmaster/VarifocalNet

Scale-aware Automatic Augmentation for Object Detection

• Paper: https://arxiv.org/abs/2103.17220

• Code: https://github.com/Jia-Research-Lab/SA-AutoAug

OTA: Optimal Transport Assignment for Object Detection

• Paper: https://arxiv.org/abs/2103.14259
• Code: https://github.com/Megvii-BaseDetection/OTA

Distilling Object Detectors via Decoupled Features

• Paper: https://arxiv.org/abs/2103.14475
• Code: https://github.com/ggjy/DeFeat.pytorch

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

• Paper: https://arxiv.org/abs/2011.12450
• Code: https://github.com/PeizeSun/SparseR-CNN

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge

• Homepage: https://rl.uni-freiburg.de/
• Paper: https://arxiv.org/abs/2103.01353
• Code: None

Positive-Unlabeled Data Purification in the Wild for Object Detection

• Paper: None
• Code: None

Instance Localization for Self-supervised Detection Pretraining

• Paper: https://arxiv.org/abs/2102.08318
• Code: https://github.com/limbo0000/InstanceLoc

MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection

• Paper: https://arxiv.org/abs/2103.04224
• Code: None

End-to-End Object Detection with Fully Convolutional Network

• Paper: https://arxiv.org/abs/2012.03544
• Code: https://github.com/Megvii-BaseDetection/DeFCN

Robust and Accurate Object Detection via Adversarial Learning

• Paper: https://arxiv.org/abs/2103.13886

• Code: None

I^3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors

• Paper: https://arxiv.org/abs/2103.13757
• Code: None

Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

• Paper: https://arxiv.org/abs/2103.11402
• Code: None

OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection

• Paper: https://arxiv.org/abs/2103.04507
• Code: https://github.com/VDIGPKU/OPANAS

YOLOF：You Only Look One-level Feature

• Paper: https://arxiv.org/abs/2103.09460
• Code: https://github.com/megvii-model/YOLOF

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

• Paper(Oral): https://arxiv.org/abs/2011.09094
• Code: https://github.com/dddzg/up-detr

General Instance Distillation for Object Detection

• Paper: https://arxiv.org/abs/2103.02340
• Code: None

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge

• Homepage: http://rl.uni-freiburg.de/research/multimodal-distill
• Paper: https://arxiv.org/abs/2103.01353
• Code: http://rl.uni-freiburg.de/research/multimodal-distill

Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

• Paper: https://arxiv.org/abs/2011.12885
• Code: https://github.com/implus/GFocalV2

Multiple Instance Active Learning for Object Detection

• Paper: https://github.com/yuantn/MIAL/raw/master/paper.pdf
• Code: https://github.com/yuantn/MIAL

Towards Open World Object Detection

• Paper(Oral): https://arxiv.org/abs/2103.02603
• Code: https://github.com/JosephKJ/OWOD

Few-Shot物体検出

Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection

• Paper: https://arxiv.org/abs/2103.17115
• Code: https://github.com/hzhupku/DCNet

Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection

• Paper: https://arxiv.org/abs/2103.01903
• Code: None

Few-Shot Object Detection via Contrastive Proposal Encoding

• Paper: https://arxiv.org/abs/2103.05950
• Code: https://github.com/MegviiDetection/FSCE

旋转物体検出

ReDet: A Rotation-equivariant Detector for Aerial Object Detection

• Paper: https://arxiv.org/abs/2103.07733

• Code: https://github.com/csuhan/ReDet

单/多物体追跡(Object Tracking)

单物体追跡

Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark

• Homepage: https://sites.google.com/view/langtrackbenchmark/

• Paper: https://arxiv.org/abs/2103.16746

• Evaluation Toolkit: https://github.com/wangxiao5791509/TNL2K_evaluation_toolkit

• Demo video: https://www.youtube.com/watch?v=7lvVDlkkff0&ab_channel=XiaoWang

IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking

• Paper: https://arxiv.org/abs/2103.14938
• Code: https://github.com/VISION-SJTU/IoUattack

Graph Attention Tracking

• Paper: https://arxiv.org/abs/2011.11204
• Code: https://github.com/ohhhyeahhh/SiamGAT

Rotation Equivariant Siamese Networks for Tracking

• Paper: https://arxiv.org/abs/2012.13078
• Code: None

Track to Detect and Segment: An Online Multi-Object Tracker

• Homepage: https://jialianwu.com/projects/TraDeS.html
• Paper: None
• Code: None

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking

• Paper(Oral): https://arxiv.org/abs/2103.11681

• Code: https://github.com/594422814/TransformerTrack

Transformer Tracking

• Paper: https://arxiv.org/abs/2103.15436
• Code: https://github.com/chenxin-dlut/TransT

多物体追跡

Multiple Object Tracking with Correlation Learning

• Paper: https://arxiv.org/abs/2104.03541
• Code: None

Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

• Paper: https://arxiv.org/abs/2012.02337
• Code: None

Learning a Proposal Classifier for Multiple Object Tracking

• Paper: https://arxiv.org/abs/2103.07889
• Code: https://github.com/daip13/LPC_MOT.git

Track to Detect and Segment: An Online Multi-Object Tracker

• Homepage: https://jialianwu.com/projects/TraDeS.html
• Paper: https://arxiv.org/abs/2103.08808
• Code: https://github.com/JialianW/TraDeS

セマンティックセグメンテーション(Semantic Segmentation)

Progressive Semantic Segmentation

• Paper: https://arxiv.org/abs/2104.03778
• Code: https://github.com/VinAIResearch/MagNet

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

• Paper: https://arxiv.org/abs/2012.15840
• Code: https://github.com/fudan-zvg/SETR

Bidirectional Projection Network for Cross Dimension Scene Understanding

• Paper(Oral): https://arxiv.org/abs/2103.14326
• Code: https://github.com/wbhu/BPNet

Cross-Dataset Collaborative Learning for Semantic Segmentation

• Paper: https://arxiv.org/abs/2103.11351
• Code: None

Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations

• Paper: https://arxiv.org/abs/2103.06342
• Code: None

Capturing Omni-Range Context for Omnidirectional Segmentation

• Paper: https://arxiv.org/abs/2103.05687
• Code: None

Learning Statistical Texture for Semantic Segmentation

• Paper: https://arxiv.org/abs/2103.04133
• Code: None

PLOP: Learning without Forgetting for Continual Semantic Segmentation

• Paper: https://arxiv.org/abs/2011.11390
• Code: None

弱监督セマンティックセグメンテーション

Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation

• Homepage: https://cvlab.yonsei.ac.kr/projects/BANA/

• Paper: https://arxiv.org/abs/2104.00905

• Code: None

Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

• Paper: https://arxiv.org/abs/2103.14581
• Code: None

BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation

• Paper: https://arxiv.org/abs/2103.08907
• Code: None

半监督セマンティックセグメンテーション

Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation

• Paper: https://arxiv.org/abs/2103.04705

域自适应セマンティックセグメンテーション

RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

• Paper: https://arxiv.org/abs/2103.15597
• Code: https://github.com/shachoi/RobustNet

Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization

• Paper: https://arxiv.org/abs/2103.13041
• Code: None

MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

• Paper: https://arxiv.org/abs/2103.05254
• Code: None

Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation

• Paper: https://arxiv.org/abs/2103.04717
• Code: None

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation

• Paper: https://arxiv.org/abs/2101.10979
• Code: https://github.com/microsoft/ProDA

動画セマンティックセグメンテーション

VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild

• Homepage: https://www.vspwdataset.com/
• Paper: https://www.vspwdataset.com/CVPR2021__miao.pdf
• GitHub: https://github.com/sssdddwww2/vspw_dataset_download

インスタンスセグメンテーション(Instance Segmentation)

RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features

• Paper: https://arxiv.org/abs/2104.08569
• Code: https://github.com/zhanggang001/RefineMask/

Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation

• Paper: https://arxiv.org/abs/2104.05239
• Code: https://github.com/tinyalpha/BPR

Multi-Scale Aligned Distillation for Low-Resolution Detection

• Paper: https://jiaya.me/papers/ms_align_distill_cvpr21.pdf

• Code: https://github.com/Jia-Research-Lab/MSAD

Boundary IoU: Improving Object-Centric Image Segmentation Evaluation

• Homepage: https://bowenc0221.github.io/boundary-iou/

• Paper: https://arxiv.org/abs/2103.16562

• Code: https://github.com/bowenc0221/boundary-iou-api

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers

• Paper: https://arxiv.org/abs/2103.12340

• Code: https://github.com/lkeab/BCNet

Zero-shot instance segmentation（Not Sure）

• Paper: None
• Code: https://github.com/CVPR2021-pape-id-1395/CVPR2021-paper-id-1395

動画インスタンスセグメンテーション

STMask: Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation

• Paper: http://www4.comp.polyu.edu.hk/~cslzhang/papers.htm
• Code: https://github.com/MinghanLi/STMask

End-to-End Video Instance Segmentation with Transformers

• Paper(Oral): https://arxiv.org/abs/2011.14503
• Code: https://github.com/Epiphqny/VisTR

全景分割(Panoptic Segmentation)

Panoptic Segmentation Forecasting

• Paper: https://arxiv.org/abs/2104.03962
• Code: https://github.com/nianticlabs/panoptic-forecasting

Fully Convolutional Networks for Panoptic Segmentation

• Paper: https://arxiv.org/abs/2012.00720

• Code: https://github.com/yanwei-li/PanopticFCN

Cross-View Regularization for Domain Adaptive Panoptic Segmentation

• Paper: https://arxiv.org/abs/2103.02584
• Code: None

医学画像分割

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

• Paper: https://arxiv.org/abs/2103.06030
• Code: https://github.com/liuquande/FedDG-ELCFS

3D医学画像分割

DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation

• Paper(Oral): https://arxiv.org/abs/2103.15954
• Code: None

動画物体分割(Video-Object-Segmentation)

Learning Position and Target Consistency for Memory-based Video Object Segmentation

• Paper: https://arxiv.org/abs/2104.04329
• Code: None

交互式動画物体分割(Interactive-Video-Object-Segmentation)

Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

• Homepage: https://hkchengrex.github.io/MiVOS/

• Paper: https://arxiv.org/abs/2103.07941

• Code: https://github.com/hkchengrex/MiVOS

• Demo: https://hkchengrex.github.io/MiVOS/video.html#partb

Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

• Paper: https://arxiv.org/abs/2103.10391

• Code: https://github.com/svip-lab/IVOS-W

显著性検出(Saliency Detection)

Uncertainty-aware Joint Salient Object and Camouflaged Object Detection

• Paper: https://arxiv.org/abs/2104.02628

• Code: https://github.com/JingZhang617/Joint_COD_SOD

Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion

• Paper(Oral): https://arxiv.org/abs/2103.11832
• Code: https://github.com/sunpeng1996/DSA2F

伪装物体検出(Camouflaged Object Detection)

Uncertainty-aware Joint Salient Object and Camouflaged Object Detection

• Paper: https://arxiv.org/abs/2104.02628

• Code: https://github.com/JingZhang617/Joint_COD_SOD

协同显著性検出(Co-Salient Object Detection)

Group Collaborative Learning for Co-Salient Object Detection

• Paper: https://arxiv.org/abs/2104.01108
• Code: https://github.com/fanq15/GCoNet

协同显著性検出(Image Matting)

Semantic Image Matting

• Paper: https://arxiv.org/abs/2104.08201
• Code: https://github.com/nowsyn/SIM
• Dataset: https://github.com/nowsyn/SIM

歩行者重識別(Person Re-identification)

Combined Depth Space based Architecture Search For Person Re-identification

• Paper: https://arxiv.org/abs/2104.04163
• Code: None

歩行者搜索(Person Search)

Anchor-Free Person Search

• Paper: https://arxiv.org/abs/2103.11617
• Code: https://github.com/daodaofr/AlignPS
• Interpretation: 首个无需锚框（Anchor-Free）的歩行者搜索框架 | CVPR 2021

動画理解/行動識別(Video Understanding)

No frame left behind: Full Video Action Recognition

• Paper: https://arxiv.org/abs/2103.15395
• Code: None

Learning Salient Boundary Feature for Anchor-free Temporal Action Localization

• Paper: https://arxiv.org/abs/2103.13137
• Code: None

Temporal Context Aggregation Network for Temporal Action Proposal Refinement

• Paper: https://arxiv.org/abs/2103.13141
• Code: None
• Interpretation: CVPR 2021 | TCANet：最强时序动作提名修正网络

ACTION-Net: Multipath Excitation for Action Recognition

• Paper: https://arxiv.org/abs/2103.07372
• Code: https://github.com/V-Sense/ACTION-Net

Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning

• Homepage: https://fingerrec.github.io/index_files/jinpeng/papers/CVPR2021/project_website.html
• Paper: https://arxiv.org/abs/2009.05769
• Code: https://github.com/FingerRec/BE

TDN: Temporal Difference Networks for Efficient Action Recognition

• Paper: https://arxiv.org/abs/2012.10071
• Code: https://github.com/MCG-NJU/TDN

顔識別(Face Recognition)

A 3D GAN for Improved Large-pose Facial Recognition

• Paper: https://arxiv.org/abs/2012.10545
• Code: None

MagFace: A Universal Representation for Face Recognition and Quality Assessment

• Paper(Oral): https://arxiv.org/abs/2103.06627
• Code: https://github.com/IrvingMeng/MagFace

WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition

• Homepage: https://www.face-benchmark.org/
• Paper: https://arxiv.org/abs/2103.04098
• Dataset: https://www.face-benchmark.org/

When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework

• Paper(Oral): https://arxiv.org/abs/2103.01520
• Code: https://github.com/Hzzone/MTLFace
• Dataset: https://github.com/Hzzone/MTLFace

顔検出(Face Detection)

HLA-Face: Joint High-Low Adaptation for Low Light Face Detection

• Homepage: https://daooshee.github.io/HLA-Face-Website/
• Paper: https://arxiv.org/abs/2104.01984
• Code: https://github.com/daooshee/HLA-Face-Code

CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement

• Paper: https://arxiv.org/abs/2103.07017
• Code: None

Face Anti-spoofing(Face Anti-Spoofing)

Cross Modal Focal Loss for RGBD Face Anti-Spoofing

• Paper: https://arxiv.org/abs/2103.00948
• Code: None

Deepfake検出(Deepfake Detection)

Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

• Paper：https://arxiv.org/abs/2103.01856
• Code: None

Multi-attentional Deepfake Detection

• Paper：https://arxiv.org/abs/2103.02406
• Code: None

顔年龄推定(Age Estimation)

PML: Progressive Margin Loss for Long-tailed Age Classification

• Paper: https://arxiv.org/abs/2103.02140
• Code: None

顔表情識別(Facial Expression Recognition)

Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition

• Paper: https://arxiv.org/abs/2103.13372
• Code: None

Deepfakes

MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes

• Paper: https://arxiv.org/abs/2103.14211
• Code: None

人体解析(Human Parsing)

Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing

• Paper: https://arxiv.org/abs/2103.04570
• Code: https://github.com/tfzhou/MG-HumanParsing

2D/3D人体姿勢推定(2D/3D Human Pose Estimation)

2D 人体姿勢推定

Pose Recognition with Cascade Transformers

• Paper: https://arxiv.org/abs/2104.06976

• Code: https://github.com/mlpc-ucsd/PRTR

DCPose: Deep Dual Consecutive Network for Human Pose Estimation

• Paper: https://arxiv.org/abs/2103.07254
• Code: https://github.com/Pose-Group/DCPose

3D 人体姿勢推定

Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration

• Paper: https://arxiv.org/abs/2103.02845
• Code: https://github.com/SeanChenxy/HandMesh

Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks

• Paper: https://arxiv.org/abs/2104.01797
• https://github.com/3dpose/3D-Multi-Person-Pose

HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation

• Homepage: https://jeffli.site/HybrIK/
• Paper: https://arxiv.org/abs/2011.14672
• Code: https://github.com/Jeff-sjtu/HybrIK

动物姿勢推定(Animal Pose Estimation)

From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation

• Paper: https://arxiv.org/abs/2103.14843
• Code: None

Human Volumetric Capture

POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture

• Homepage: http://www.liuyebin.com/posefusion/posefusion.html

• Paper(Oral): https://arxiv.org/abs/2103.15331

• Code: None

シーンテキスト検出(Scene Text Detection)

Fourier Contour Embedding for Arbitrary-Shaped Text Detection

• Paper: https://arxiv.org/abs/2104.10442
• Code: None

シーンテキスト識別(Scene Text Recognition)

Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

• Paper: https://arxiv.org/abs/2103.06495
• Code: https://github.com/FangShancheng/ABINet

画像圧縮

Checkerboard Context Model for Efficient Learned Image Compression

• Paper: https://arxiv.org/abs/2103.15306
• Code: None

Slimmable Compressive Autoencoders for Practical Neural Image Compression

• Paper: https://arxiv.org/abs/2103.15726
• Code: None

Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton

• Paper: https://arxiv.org/abs/2103.15368
• Code: None

モデル圧縮/枝刈り/量化

Teachers Do More Than Teach: Compressing Image-to-Image Models

• Paper: https://arxiv.org/abs/2103.03467
• Code: https://github.com/snap-research/CAT

モデル枝刈り

Dynamic Slimmable Network

• Paper: https://arxiv.org/abs/2103.13258
• Code: https://github.com/changlin31/DS-Net

モデル量化

Network Quantization with Element-wise Gradient Scaling

• Paper: https://arxiv.org/abs/2104.00903
• Code: None

Zero-shot Adversarial Quantization

• Paper(Oral): https://arxiv.org/abs/2103.15263
• Code: https://git.io/Jqc0y

Learnable Companding Quantization for Accurate Low-bit Neural Networks

• Paper: https://arxiv.org/abs/2103.07156
• Code: None

知识蒸馏(Knowledge Distillation)

Distilling Knowledge via Knowledge Review

• Paper: https://arxiv.org/abs/2104.09044
• Code: https://github.com/Jia-Research-Lab/ReviewKD

Distilling Object Detectors via Decoupled Features

• Paper: https://arxiv.org/abs/2103.14475
• Code: https://github.com/ggjy/DeFeat.pytorch

超解像(Super-Resolution)

Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline

• Homepage: http://mepro.bjtu.edu.cn/resource.html
• Paper: https://arxiv.org/abs/2104.06174
• Code: None

ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

• Paper: https://arxiv.org/abs/2103.04039
• Code: https://github.com/Xiangtaokong/ClassSR

AdderSR: Towards Energy Efficient Image Super-Resolution

• Paper: https://arxiv.org/abs/2009.08891
• Code: None

去雾(Dehazing)

Contrastive Learning for Compact Single Image Dehazing

• Paper: https://arxiv.org/abs/2104.09367
• Code: https://github.com/GlassyWu/AECR-Net

動画超解像

Temporal Modulation Network for Controllable Space-Time Video Super-Resolution

• Paper: None
• Code: https://github.com/CS-GangXu/TMNet

画像恢复(Image Restoration)

Multi-Stage Progressive Image Restoration

• Paper: https://arxiv.org/abs/2102.02808
• Code: https://github.com/swz30/MPRNet

画像补全(Image Inpainting)

TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations

• Homepage: https://yzhouas.github.io/projects/TransFill/index.html
• Paper: https://arxiv.org/abs/2103.15982
• Code: None

PD-GAN: Probabilistic Diverse GAN for Image Inpainting

• Paper: http://raywzy.com/
• Code: http://raywzy.com/

画像编辑(Image Editing)

High-Fidelity and Arbitrary Face Editing

• Paper: https://arxiv.org/abs/2103.15814
• Code: None

Anycost GANs for Interactive Image Synthesis and Editing

• Paper: https://arxiv.org/abs/2103.03243
• Code: https://github.com/mit-han-lab/anycost-gan

PISE: Person Image Synthesis and Editing with Decoupled GAN

• Paper: https://arxiv.org/abs/2103.04023
• Code: https://github.com/Zhangjinso/PISE

DeFLOCNet: Deep Image Editing via Flexible Low-level Controls

• Paper: http://raywzy.com/
• Code: http://raywzy.com/

Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

• Paper: None
• Code: None

画像匹配(Image Matcing)

LoFTR: Detector-Free Local Feature Matching with Transformers

• Homepage: https://zju3dv.github.io/loftr/
• Paper: https://arxiv.org/abs/2104.00680
• Code: https://github.com/zju3dv/LoFTR

Convolutional Hough Matching Networks

• Homapage: http://cvlab.postech.ac.kr/research/CHM/
• Paper(Oral): https://arxiv.org/abs/2103.16831
• Code: None

画像融合(Image Blending)

Bridging the Visual Gap: Wide-Range Image Blending

• Paper: https://arxiv.org/abs/2103.15149

• Code: https://github.com/julia0607/Wide-Range-Image-Blending

反光去除(Reflection Removal)

Robust Reflection Removal with Reflection-free Flash-only Cues

• Paper: https://arxiv.org/abs/2103.04273
• Code: https://github.com/ChenyangLEI/flash-reflection-removal

3D点群分類(3D Point Clouds Classification)

Equivariant Point Network for 3D Point Cloud Analysis

• Paper: https://arxiv.org/abs/2103.14147
• Code: None

PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

• Paper: https://arxiv.org/abs/2103.14635
• Code: https://github.com/CVMI-Lab/PAConv

3D物体検出(3D Object Detection)

Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

• Paper: https://arxiv.org/abs/2104.06114
• Code: https://github.com/cheng052/BRNet

HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection

• Homepage: https://cvlab.yonsei.ac.kr/projects/HVPR/

• Paper: https://arxiv.org/abs/2104.00902

• Code: https://github.com/cvlab-yonsei/HVPR

LiDAR R-CNN: An Efficient and Universal 3D Object Detector

• Paper: https://arxiv.org/abs/2103.15297
• Code: https://github.com/tusimple/LiDAR_RCNN

M3DSSD: Monocular 3D Single Stage Object Detector

• Paper: https://arxiv.org/abs/2103.13164

• Code: https://github.com/mumianyuxin/M3DSSD

SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud

• Paper: None
• Code: https://github.com/Vegeta2020/SE-SSD

Center-based 3D Object Detection and Tracking

• Paper: https://arxiv.org/abs/2006.11275
• Code: https://github.com/tianweiy/CenterPoint

Categorical Depth Distribution Network for Monocular 3D Object Detection

• Paper: https://arxiv.org/abs/2103.01100
• Code: None

3Dセマンティックセグメンテーション(3D Semantic Segmentation)

Bidirectional Projection Network for Cross Dimension Scene Understanding

• Paper(Oral): https://arxiv.org/abs/2103.14326
• Code: https://github.com/wbhu/BPNet

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

• Paper: https://arxiv.org/abs/2103.07074
• Code: https://github.com/ShiQiu0419/BAAF-Net

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

• Paper: https://arxiv.org/abs/2011.10033
• Code: https://github.com/xinge008/Cylinder3D

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

• Homepage: https://github.com/QingyongHu/SensatUrban
• Paper: http://arxiv.org/abs/2009.03137
• Code: https://github.com/QingyongHu/SensatUrban
• Dataset: https://github.com/QingyongHu/SensatUrban

3D全景分割(3D Panoptic Segmentation)

Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation

• Paper: https://arxiv.org/abs/2103.14962
• Code: https://github.com/edwardzhou130/Panoptic-PolarNet

3D物体追跡(3D Object Trancking)

Center-based 3D Object Detection and Tracking

• Paper: https://arxiv.org/abs/2006.11275
• Code: https://github.com/tianweiy/CenterPoint

3D点群レジストレーション(3D Point Cloud Registration)

ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning

• Paper: https://arxiv.org/abs/2103.15231
• Code: None

PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

• Paper: https://arxiv.org/abs/2103.05465
• Code: https://github.com/XuyangBai/PointDSC

PREDATOR: Registration of 3D Point Clouds with Low Overlap

• Paper: https://arxiv.org/abs/2011.13005
• Code: https://github.com/ShengyuH/OverlapPredator

3D点群补全(3D Point Cloud Completion)

Variational Relational Point Completion Network

• Homepage: https://paul007pl.github.io/projects/VRCNet
• Paper: https://arxiv.org/abs/2104.10154
• Code: https://github.com/paul007pl/VRCNet

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion

• Homepage: https://alphapav.github.io/SpareNet/

• Paper: https://arxiv.org/abs/2103.02535

• Code: https://github.com/microsoft/SpareNet

3D重建(3D Reconstruction)

Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

• Paper: https://arxiv.org/abs/2104.00858
• Code: None

NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video

• Homepage: https://zju3dv.github.io/neuralrecon/

• Paper(Oral): https://arxiv.org/abs/2104.00681

• Code: https://github.com/zju3dv/NeuralRecon

6D位姿推定(6D Pose Estimation)

FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism

• Paper(Oral): https://arxiv.org/abs/2103.07054
• Code: https://github.com/DC1991/FS-Net

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

• Paper: http://arxiv.org/abs/2102.12145
• code: https://git.io/GDR-Net

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

• Paper: https://arxiv.org/abs/2103.02242
• Code: https://github.com/ethnhe/FFB6D

相机姿勢推定

Back to the Feature: Learning Robust Camera Localization from Pixels to Pose

• Paper: https://arxiv.org/abs/2103.09213
• Code: https://github.com/cvg/pixloc

深度推定(Depth Estimation)

S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation

• Paper(Oral): https://arxiv.org/abs/2104.00877
• Code: None

Beyond Image to Depth: Improving Depth Prediction using Echoes

• Homepage: https://krantiparida.github.io/projects/bimgdepth.html
• Paper: https://arxiv.org/abs/2103.08468
• Code: https://github.com/krantiparida/beyond-image-to-depth

S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation

• Paper: https://arxiv.org/abs/2103.02396
• Code: None

Depth from Camera Motion and Object Detection

• Paper: https://arxiv.org/abs/2103.01468
• Code: https://github.com/griffbr/ODMD
• Dataset: https://github.com/griffbr/ODMD

深度推定(Stereo Matching)

A Decomposition Model for Stereo Matching

• Paper: https://arxiv.org/abs/2104.07516
• Code: None

光流推定(Flow Estimation)

Learning Optical Flow From Still Images

• Homepage: https://mattpoggi.github.io/projects/cvpr2021aleotti/

• Paper: https://mattpoggi.github.io/assets/papers/aleotti2021cvpr.pdf

• Code: https://github.com/mattpoggi/depthstillation

FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds

• Paper: https://arxiv.org/abs/2104.00798
• Code: None

軌跡予測(Trajectory Prediction)

Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction

• Paper(Oral): https://arxiv.org/abs/2104.08277
• Code: None

对抗样本

LiBRe: A Practical Bayesian Approach to Adversarial Detection

• Paper: https://arxiv.org/abs/2103.14835
• Code: None

Natural Adversarial Examples

• Paper: https://arxiv.org/abs/1907.07174
• Code: https://github.com/hendrycks/natural-adv-examples

画像检索(Image Retrieval)

StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval

• Paper: https://arxiv.org/abs/2103.15706
• COde: None

QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval

• Paper: https://arxiv.org/abs/2103.02927
• Code: None

動画检索(Video Retrieval)

On Semantic Similarity in Video Retrieval

• Paper: https://arxiv.org/abs/2103.10095

• Homepage: https://mwray.github.io/SSVR/

• Code: https://github.com/mwray/Semantic-Video-Retrieval

跨模态检索(Cross-modal Retrieval)

Cross-Modal Center Loss for 3D Cross-Modal Retrieval

• Paper: https://arxiv.org/abs/2008.03561
• Code: https://github.com/LongLong-Jing/Cross-Modal-Center-Loss

Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers

• Paper: https://arxiv.org/abs/2103.16553
• Code: None

Revamping cross-modal recipe retrieval with hierarchical Transformers and self-supervised learning

• Paper: https://www.amazon.science/publications/revamping-cross-modal-recipe-retrieval-with-hierarchical-transformers-and-self-supervised-learning

• Code: https://github.com/amzn/image-to-recipe-transformers

Zero-Shot Learning

Counterfactual Zero-Shot and Open-Set Visual Recognition

• Paper: https://arxiv.org/abs/2103.00887
• Code: https://github.com/yue-zhongqi/gcm-cf

联邦学习(Federated Learning)

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

• Paper: https://arxiv.org/abs/2103.06030
• Code: https://github.com/liuquande/FedDG-ELCFS

動画插帧(Video Frame Interpolation)

CDFI: Compression-Driven Network Design for Frame Interpolation

• Paper: None
• Code: https://github.com/tding1/CDFI

FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation

• Homepage: https://tarun005.github.io/FLAVR/

• Paper: https://arxiv.org/abs/2012.08512

• Code: https://github.com/tarun005/FLAVR

视觉推理(Visual Reasoning)

Transformation Driven Visual Reasoning

• homepage: https://hongxin2019.github.io/TVR/
• Paper: https://arxiv.org/abs/2011.13160
• Code: https://github.com/hughplay/TVR

视图合成(View Synthesis)

Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

• Homepage: https://virtualhumans.mpi-inf.mpg.de/srf/
• Paper: https://arxiv.org/abs/2104.06935

Self-Supervised Visibility Learning for Novel View Synthesis

• Paper: https://arxiv.org/abs/2103.15407
• Code: None

NeX: Real-time View Synthesis with Neural Basis Expansion

• Homepage: https://nex-mpi.github.io/
• Paper(Oral): https://arxiv.org/abs/2103.05606

スタイル変換(Style Transfer)

Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer

• Paper: https://arxiv.org/abs/2104.05376
• Code: https://github.com/PaddlePaddle/PaddleGAN/

布局生成(Layout Generation)

Variational Transformer Networks for Layout Generation

• Paper: https://arxiv.org/abs/2104.02416
• Code: None

Domain Generalization

RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

• Paper: https://arxiv.org/abs/2103.15597
• Code: https://github.com/shachoi/RobustNet

Adaptive Methods for Real-World Domain Generalization

• Paper: https://arxiv.org/abs/2103.15796
• Code: None

FSDR: Frequency Space Domain Randomization for Domain Generalization

• Paper: https://arxiv.org/abs/2103.02370
• Code: None

Domain Adaptation

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

• Paper: https://arxiv.org/abs/2104.00808
• Code: None

Domain Consensus Clustering for Universal Domain Adaptation

• Paper: http://reler.net/papers/guangrui_cvpr2021.pdf
• Code: https://github.com/Solacex/Domain-Consensus-Clustering

Open-Set Recognition

Learning Placeholders for Open-Set Recognition

• Paper(Oral): https://arxiv.org/abs/2103.15086
• Code: None

Adversarial Attack

IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking

• Paper: https://arxiv.org/abs/2103.14938
• Code: https://github.com/VISION-SJTU/IoUattack

Human-Object Interaction (HOI)検出

Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information

• Paper: https://arxiv.org/abs/2103.05399
• Code: https://github.com/hitachi-rd-cv/qpic

Reformulating HOI Detection as Adaptive Set Prediction

• Paper: https://arxiv.org/abs/2103.05983
• Code: https://github.com/yoyomimi/AS-Net

Detecting Human-Object Interaction via Fabricated Compositional Learning

• Paper: https://arxiv.org/abs/2103.08214
• Code: https://github.com/zhihou7/FCL

End-to-End Human Object Interaction Detection with HOI Transformer

• Paper: https://arxiv.org/abs/2103.04503
• Code: https://github.com/bbepoch/HoiTransformer

阴影去除(Shadow Removal)

Auto-Exposure Fusion for Single-Image Shadow Removal

• Paper: https://arxiv.org/abs/2103.01255
• Code: https://github.com/tsingqguo/exposure-fusion-shadow-removal

虚拟换衣(Virtual Try-On)

Parser-Free Virtual Try-on via Distilling Appearance Flows

基于外观流蒸馏的无需人体解析的虚拟换装

• Paper: https://arxiv.org/abs/2103.04559
• Code: https://github.com/geyuying/PF-AFN

データセット(Datasets)

Learning To Count Everything

• Paper: https://arxiv.org/abs/2104.08391
• Code: https://github.com/cvlab-stonybrook/LearningToCountEverything
• Dataset: https://github.com/cvlab-stonybrook/LearningToCountEverything

Semantic Image Matting

• Paper: https://arxiv.org/abs/2104.08201
• Code: https://github.com/nowsyn/SIM
• Dataset: https://github.com/nowsyn/SIM

Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline

• Homepage: http://mepro.bjtu.edu.cn/resource.html
• Paper: https://arxiv.org/abs/2104.06174
• Code: None

Visual Semantic Role Labeling for Video Understanding

• Homepage: https://vidsitu.org/

• Paper: https://arxiv.org/abs/2104.00990

• Code: https://github.com/TheShadow29/VidSitu

• Dataset: https://github.com/TheShadow29/VidSitu

VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild

• Homepage: https://www.vspwdataset.com/
• Paper: https://www.vspwdataset.com/CVPR2021__miao.pdf
• GitHub: https://github.com/sssdddwww2/vspw_dataset_download

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

• Homepage: https://vap.aau.dk/sewer-ml/
• Paper: https://arxiv.org/abs/2103.10619

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

• Homepage: https://vap.aau.dk/sewer-ml/

• Paper: https://arxiv.org/abs/2103.10895

Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food

• Paper: https://arxiv.org/abs/2103.03375
• Dataset: None

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

• Homepage: https://github.com/QingyongHu/SensatUrban
• Paper: http://arxiv.org/abs/2009.03137
• Code: https://github.com/QingyongHu/SensatUrban
• Dataset: https://github.com/QingyongHu/SensatUrban

When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework

• Paper(Oral): https://arxiv.org/abs/2103.01520
• Code: https://github.com/Hzzone/MTLFace
• Dataset: https://github.com/Hzzone/MTLFace

Depth from Camera Motion and Object Detection

• Paper: https://arxiv.org/abs/2103.01468
• Code: https://github.com/griffbr/ODMD
• Dataset: https://github.com/griffbr/ODMD

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge

• Homepage: http://rl.uni-freiburg.de/research/multimodal-distill
• Paper: https://arxiv.org/abs/2103.01353
• Code: http://rl.uni-freiburg.de/research/multimodal-distill

Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

• Paper: https://arxiv.org/abs/2012.02206

• Code: https://github.com/daveredrum/Scan2Cap

• Dataset: https://github.com/daveredrum/ScanRefer

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge

• Paper: https://arxiv.org/abs/2103.01353
• Code: http://rl.uni-freiburg.de/research/multimodal-distill
• Dataset: http://rl.uni-freiburg.de/research/multimodal-distill

その他(Others)

KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control

• Homepage: http://tomasjakab.github.io/KeypointDeformer

• Paper(Oral): https://arxiv.org/abs/2104.11224

• Code: https://github.com/tomasjakab/keypoint_deformer/

Learning To Count Everything

• Paper: https://arxiv.org/abs/2104.08391
• Code: https://github.com/cvlab-stonybrook/LearningToCountEverything
• Dataset: https://github.com/cvlab-stonybrook/LearningToCountEverything

SOLD2: Self-supervised Occlusion-aware Line Description and Detection

• Paper(Oral): https://arxiv.org/abs/2104.03362
• Code: https://github.com/cvg/SOLD2

Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression

• Homepage: https://li-wanhua.github.io/POEs/
• Paper: https://arxiv.org/abs/2103.13629
• Code: https://github.com/Li-Wanhua/POEs

LEAP: Learning Articulated Occupancy of People

• Paper: https://arxiv.org/abs/2104.06849
• Code: None

Visual Semantic Role Labeling for Video Understanding

• Homepage: https://vidsitu.org/

• Paper: https://arxiv.org/abs/2104.00990

• Code: https://github.com/TheShadow29/VidSitu

• Dataset: https://github.com/TheShadow29/VidSitu

UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

• Paper: https://arxiv.org/abs/2104.00946
• Code: https://github.com/SUTDCV/UAV-Human

Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning

• Paper(Oral): https://arxiv.org/abs/2104.00924
• Code: None

Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction

• Paper: https://arxiv.org/abs/2104.00858
• Code: None

Towards High Fidelity Face Relighting with Realistic Shadows

• Paper: https://arxiv.org/abs/2104.00825
• Code: None

BRepNet: A topological message passing system for solid models

• Paper(Oral): https://arxiv.org/abs/2104.00706
• Code: None

Visually Informed Binaural Audio Generation without Binaural Audios

• Homepage: https://sheldontsui.github.io/projects/PseudoBinaural

• Paper: None

• GitHub: https://github.com/SheldonTsui/PseudoBinaural_CVPR2021

• Demo: https://www.youtube.com/watch?v=r-uC2MyAWQc

Exploring intermediate representation for monocular vehicle pose estimation

• Paper: None
• Code: https://github.com/Nicholasli1995/EgoNet

Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB

• Paper(Oral): https://arxiv.org/abs/2103.14708
• Code: None

Invertible Image Signal Processing

• Paper: https://arxiv.org/abs/2103.15061
• Code: https://github.com/yzxing87/Invertible-ISP

Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling

• Paper: https://arxiv.org/abs/2103.14858
• Code: None

SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

• Paper: https://arxiv.org/abs/2103.14898
• Code: None

Embedding Transfer with Label Relaxation for Improved Metric Learning

• Paper: https://arxiv.org/abs/2103.14908
• Code: None

Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

• Paper: https://arxiv.org/abs/2103.15076
• Code: https://github.com/hlei-ziyan/Picasso

Meta-Mining Discriminative Samples for Kinship Verification

• Paper: https://arxiv.org/abs/2103.15108
• Code: None

Cloud2Curve: Generation and Vectorization of Parametric Sketches

• Paper: https://arxiv.org/abs/2103.15536
• Code: None

TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

• Paper: https://arxiv.org/abs/2103.15538
• Code: https://github.com/SUTDCV/SUTD-TrafficQA

Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution

• Homepage: http://wellyzhang.github.io/project/prae.html

• Paper: https://arxiv.org/abs/2103.14230

• Code: None

ACRE: Abstract Causal REasoning Beyond Covariation

• Homepage: http://wellyzhang.github.io/project/acre.html

• Paper: https://arxiv.org/abs/2103.14232

• Code: None

Confluent Vessel Trees with Accurate Bifurcations

• Paper: https://arxiv.org/abs/2103.14268
• Code: None

Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling

• Paper: https://arxiv.org/abs/2103.14338
• Code: https://github.com/HuangZhiChao95/FewShotMotionTransfer

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

• Homepage: https://paschalidoud.github.io/neural_parts
• Paper: None
• Code: https://github.com/paschalidoud/neural_parts

Knowledge Evolution in Neural Networks

• Paper(Oral): https://arxiv.org/abs/2103.05152
• Code: https://github.com/ahmdtaha/knowledge_evolution

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

• Paper: https://arxiv.org/abs/2103.02148
• Code: https://github.com/guopengf/FLMRCM

SGP: Self-supervised Geometric Perception

• Oral

• Paper: https://arxiv.org/abs/2103.03114

• Code: https://github.com/theNded/SGP

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

• Paper: https://arxiv.org/abs/2103.02148
• Code: https://github.com/guopengf/FLMRCM

Diffusion Probabilistic Models for 3D Point Cloud Generation

• Paper: https://arxiv.org/abs/2103.01458
• Code: https://github.com/luost26/diffusion-point-cloud

Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

• Paper: https://arxiv.org/abs/2012.02206

• Code: https://github.com/daveredrum/Scan2Cap

• Dataset: https://github.com/daveredrum/ScanRefer

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge

• Paper: https://arxiv.org/abs/2103.01353

• Code: http://rl.uni-freiburg.de/research/multimodal-distill

• Dataset: http://rl.uni-freiburg.de/research/multimodal-distill

待添加(TODO)

• 重磅！腾讯优图20篇論文入选CVPR 2021
• MePro团队三篇論文被CVPR 2021接收

採択されたか不明(Not Sure)

CT Film Recovery via Disentangling Geometric Deformation and Photometric Degradation: Simulated Datasets and Deep Models

• Paper: none
• Code: https://github.com/transcendentsky/Film-Recovery

Toward Explainable Reflection Removal with Distilling and Model Uncertainty

• Paper: none
• Code: https://github.com/ytpeng-aimlab/CVPR-2021-Toward-Explainable-Reflection-Removal-with-Distilling-and-Model-Uncertainty

DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation

• Paper: none
• Code: https://github.com/lhaippp/DeepOIS

Exploring Adversarial Fake Images on Face Manifold

• Paper: none
• Code: https://github.com/ldz666666/Style-atk

Uncertainty-Aware Semi-Supervised Crowd Counting via Consistency-Regularized Surrogate Task

• Paper: none
• Code: https://github.com/yandamengdanai/Uncertainty-Aware-Semi-Supervised-Crowd-Counting-via-Consistency-Regularized-Surrogate-Task

Temporal Contrastive Graph for Self-supervised Video Representation Learning

• Paper: none
• Code: https://github.com/YangLiu9208/TCG

Boosting Monocular Depth Estimation Models to High-Resolution via Context-Aware Patching

• Paper: none
• Code: https://github.com/ouranonymouscvpr/cvpr2021_ouranonymouscvpr

Fast and Memory-Efficient Compact Bilinear Pooling

• Paper: none
• Code: https://github.com/cvpr2021kp2/cvpr2021kp2

Identification of Empty Shelves in Supermarkets using Domain-inspired Features with Structural Support Vector Machine

• Paper: none
• Code: https://github.com/gapDetection/cvpr2021

Estimating A Child's Growth Potential From Cephalometric X-Ray Image via Morphology-Aware Interactive Keypoint Estimation

• Paper: none
• Code: https://github.com/interactivekeypoint2020/Morph

https://github.com/ShaoQiangShen/CVPR2021

https://github.com/gillesflash/CVPR2021

https://github.com/anonymous-submission1991/BaLeNAS

https://github.com/cvpr2021dcb/cvpr2021dcb

https://github.com/anonymousauthorCV/CVPR2021_PaperID_8578

https://github.com/AldrichZeng/FreqPrune

https://github.com/Anonymous-AdvCAM/Anonymous-AdvCAM

https://github.com/ddfss/datadrive-fss

注意事項

このリポジトリは amusi/CVPR2020-Codeを日本語訳したものです．

元のリポジトリの内容及び日本語訳は正確でないことがありますのでご注意ください．

Acknowledgments

Proofread by Jin Jiongxing