CVPR 2021 论文和开源项目合集

喜欢ヅ旅行 2022-10-13 12:50 1470阅读 0赞

# CVPR 2021 论文和开源项目合集(Papers with Code) #

地址：[https://github.com/amusi/CVPR2021-Papers-with-Code][https_github.com_amusi_CVPR2021-Papers-with-Code]

*  Best Paper
 *  Backbone
 *  NAS
 *  GAN
 *  VAE
 *  Visual Transformer
 *  Regularization
 *  SLAM
 *  长尾分布(Long-Tailed)
 *  数据增广(Data Augmentation)
 *  无监督/自监督(Self-Supervised)
 *  半监督(Semi-Supervised)
 *  胶囊网络(Capsule Network)
 *  图像分类(Image Classification
 *  2D目标检测(Object Detection)
 *  单/多目标跟踪(Object Tracking)
 *  语义分割(Semantic Segmentation)
 *  实例分割(Instance Segmentation)
 *  全景分割(Panoptic Segmentation)
 *  医学图像分割(Medical Image Segmentation)
 *  视频目标分割(Video-Object-Segmentation)
 *  交互式视频目标分割(Interactive-Video-Object-Segmentation)
 *  显著性检测(Saliency Detection)
 *  伪装物体检测(Camouflaged Object Detection)
 *  协同显著性检测(Co-Salient Object Detection)
 *  图像抠图(Image Matting)
 *  行人重识别(Person Re-identification)
 *  行人搜索(Person Search)
 *  视频理解/行为识别(Video Understanding)
 *  人脸识别(Face Recognition)
 *  人脸检测(Face Detection)
 *  人脸活体检测(Face Anti-Spoofing)
 *  Deepfake检测(Deepfake Detection)
 *  人脸年龄估计(Age-Estimation)
 *  人脸表情识别(Facial-Expression-Recognition)
 *  Deepfakes
 *  人体解析(Human Parsing)
 *  2D/3D人体姿态估计(2D/3D Human Pose Estimation)
 *  动物姿态估计(Animal Pose Estimation)
 *  手部姿态估计(Hand Pose Estimation)
 *  Human Volumetric Capture
 *  场景文本识别(Scene Text Recognition)
 *  图像压缩(Image Compression)
 *  模型压缩/剪枝/量化
 *  知识蒸馏(Knowledge Distillation)
 *  超分辨率(Super-Resolution)
 *  去雾(Dehazing)
 *  图像恢复(Image Restoration)
 *  图像补全(Image Inpainting)
 *  图像编辑(Image Editing)
 *  图像描述(Image Captioning)
 *  字体生成(Font Generation)
 *  图像匹配(Image Matching)
 *  图像融合(Image Blending)
 *  反光去除(Reflection Removal)
 *  3D点云分类(3D Point Clouds Classification)
 *  3D目标检测(3D Object Detection)
 *  3D语义分割(3D Semantic Segmentation)
 *  3D全景分割(3D Panoptic Segmentation)
 *  3D目标跟踪(3D Object Tracking)
 *  3D点云配准(3D Point Cloud Registration)
 *  3D点云补全(3D-Point-Cloud-Completion)
 *  3D重建(3D Reconstruction)
 *  6D位姿估计(6D Pose Estimation)
 *  相机姿态估计(Camera Pose Estimation)
 *  深度估计(Depth Estimation)
 *  立体匹配(Stereo Matching)
 *  光流估计(Flow Estimation)
 *  车道线检测(Lane Detection)
 *  轨迹预测(Trajectory Prediction)
 *  人群计数(Crowd Counting)
 *  对抗样本(Adversarial-Examples)
 *  图像检索(Image Retrieval)
 *  视频检索(Video Retrieval)
 *  跨模态检索(Cross-modal Retrieval)
 *  Zero-Shot Learning
 *  联邦学习(Federated Learning)
 *  视频插帧(Video Frame Interpolation)
 *  视觉推理(Visual Reasoning)
 *  图像合成(Image Synthesis)
 *  视图合成(Visual Synthesis)
 *  风格迁移(Style Transfer)
 *  布局生成(Layout Generation)
 *  Domain Generalization
 *  Domain Adaptation
 *  Open-Set
 *  Adversarial Attack
 *  "人-物"交互(HOI)检测
 *  阴影去除(Shadow Removal)
 *  虚拟试衣(Virtual Try-On)
 *  标签噪声(Label Noise)
 *  视频稳像(Video Stabilization)
 *  数据集(Datasets)
 *  其他(Others)
 *  待添加(TODO)
 *  不确定中没中(Not Sure)

# Best Paper #

**GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields**

*  Homepage: https://m-niemeyer.github.io/project-pages/giraffe/index.html
 *  Paper(Oral): https://arxiv.org/abs/2011.12100
 *  Code: https://github.com/autonomousvision/giraffe
 *  Demo: http://www.youtube.com/watch?v=fIaDXC-qRSg&vq=hd1080&autoplay=1

# Backbone #

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

*  Paper(Oral): https://arxiv.org/abs/2106.06560
 *  Code: https://github.com/dingmyu/HR-NAS

**BCNet: Searching for Network Width with Bilaterally Coupled Network**

*  Paper: https://arxiv.org/abs/2105.10533
 *  Code: None

**Decoupled Dynamic Filter Networks**

*  Homepage: https://thefoxofsky.github.io/project\_pages/ddf
 *  Paper: https://arxiv.org/abs/2104.14107
 *  Code: https://github.com/thefoxofsky/DDF

**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 #

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

*  Paper(Oral): https://arxiv.org/abs/2106.06560
 *  Code: https://github.com/dingmyu/HR-NAS

**BCNet: Searching for Network Width with Bilaterally Coupled Network**

*  Paper: https://arxiv.org/abs/2105.10533
 *  Code: None

**ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search**

*  Paper: ttps://arxiv.org/abs/2105.10154
 *  Code: None

**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: https://arxiv.org/abs/2105.12971
 *  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 #

**High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network**

*  Paper: https://arxiv.org/abs/2105.09188
 *  Code: https://github.com/csjliang/LPTN
 *  Dataset: https://github.com/csjliang/LPTN

**DG-Font: Deformable Generative Networks for Unsupervised Font Generation**

*  Paper: https://arxiv.org/abs/2104.03064
 *  Code: https://github.com/ecnuycxie/DG-Font

**PD-GAN: Probabilistic Diverse GAN for Image Inpainting**

*  Paper: https://arxiv.org/abs/2105.02201
 *  Code: https://github.com/KumapowerLIU/PD-GAN

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

*  Paper: https://arxiv.org/abs/2104.14754
 *  Code: https://github.com/naver-ai/StyleMapGAN
 *  Demo Video: https://youtu.be/qCapNyRA\_Ng

**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 #

**1. End-to-End Human Pose and Mesh Reconstruction with Transformers**

*  Paper: https://arxiv.org/abs/2012.09760
 *  Code: https://github.com/microsoft/MeshTransformer

**2. Temporal-Relational CrossTransformers for Few-Shot Action Recognition**

*  Paper: https://arxiv.org/abs/2101.06184
 *  Code: https://github.com/tobyperrett/trx

**3. Kaleido-BERT：Vision-Language Pre-training on Fashion Domain**

*  Paper: https://arxiv.org/abs/2103.16110
 *  Code: https://github.com/mczhuge/Kaleido-BERT

**4. HOTR: End-to-End Human-Object Interaction Detection with Transformers**

*  Paper: https://arxiv.org/abs/2104.13682
 *  Code: https://github.com/kakaobrain/HOTR

**5. Multi-Modal Fusion Transformer for End-to-End Autonomous Driving**

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

**6. Pose Recognition with Cascade Transformers**

*  Paper: https://arxiv.org/abs/2104.06976
 *  Code: https://github.com/mlpc-ucsd/PRTR

**7. Variational Transformer Networks for Layout Generation**

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

**8. 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

**9. 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

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

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

**11. Transformer Tracking**

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

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

*  Paper(Oral): https://arxiv.org/abs/2106.06560
 *  Code: https://github.com/dingmyu/HR-NAS

**13. MIST: Multiple Instance Spatial Transformer**

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

**14. Multimodal Motion Prediction with Stacked Transformers**

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

**15. 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

**16. Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking**

*  Paper(Oral): https://arxiv.org/abs/2103.11681
 *  Code: https://github.com/594422814/TransformerTrack

**17. Pre-Trained Image Processing Transformer**

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

**18. End-to-End Video Instance Segmentation with Transformers**

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

**19. 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

**20. End-to-End Human Object Interaction Detection with HOI Transformer**

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

**21. Transformer Interpretability Beyond Attention Visualization**

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

**22. Diverse Part Discovery: Occluded Person Re-Identification With Part-Aware Transformer**

*  Paper: None
 *  Code: None

**23. LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity**

*  Paper: None
 *  Code: None

**24. Line Segment Detection Using Transformers without Edges**

*  Paper(Oral): https://arxiv.org/abs/2101.01909
 *  Code: None

**25. MaX-DeepLab: End-to-End Panoptic Segmentation With Mask Transformers**

*  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Wang\_MaX-DeepLab\_End-to-End\_Panoptic\_Segmentation\_With\_Mask\_Transformers\_CVPR\_2021\_paper.html
 *  Code: None

**26. SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation**

*  Paper(Oral): https://arxiv.org/abs/2101.08833
 *  Code: https://github.com/dukebw/SSTVOS

**27. Facial Action Unit Detection With Transformers**

*  Paper: None
 *  Code: None

**28. Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition**

*  Paper: None
 *  Code: None

**29. Lesion-Aware Transformers for Diabetic Retinopathy Grading**

*  Paper: None
 *  Code: None

**30. Topological Planning With Transformers for Vision-and-Language Navigation**

*  Paper: https://arxiv.org/abs/2012.05292
 *  Code: None

**31. Adaptive Image Transformer for One-Shot Object Detection**

*  Paper: None
 *  Code: None

**32. Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos**

*  Paper: None
 *  Code: None

**33. Taming Transformers for High-Resolution Image Synthesis**

*  Homepage: https://compvis.github.io/taming-transformers/
 *  Paper(Oral): https://arxiv.org/abs/2012.09841
 *  Code: https://github.com/CompVis/taming-transformers

**34. Self-Supervised Video Hashing via Bidirectional Transformers**

*  Paper: None
 *  Code: None

**35. Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos**

*  Paper(Oral): https://hehefan.github.io/pdfs/p4transformer.pdf
 *  Code: None

**36. Gaussian Context Transformer**

*  Paper: None
 *  Code: None

**37. General Multi-Label Image Classification With Transformers**

*  Paper: https://arxiv.org/abs/2011.14027
 *  Code: None

**38. Bottleneck Transformers for Visual Recognition**

*  Paper: https://arxiv.org/abs/2101.11605
 *  Code: None

**39. VLN BERT: A Recurrent Vision-and-Language BERT for Navigation**

*  Paper(Oral): https://arxiv.org/abs/2011.13922
 *  Code: https://github.com/YicongHong/Recurrent-VLN-BERT

**40. Less Is More: ClipBERT for Video-and-Language Learning via Sparse Sampling**

*  Paper(Oral): https://arxiv.org/abs/2102.06183
 *  Code: https://github.com/jayleicn/ClipBERT

**41. Self-attention based Text Knowledge Mining for Text Detection**

*  Paper: None
 *  Code: https://github.com/CVI-SZU/STKM

**42. SSAN: Separable Self-Attention Network for Video Representation Learning**

*  Paper: None
 *  Code: None

**43. Scaling Local Self-Attention For Parameter Efficient Visual Backbones**

*  Paper(Oral): https://arxiv.org/abs/2103.12731
 *  Code: None

# Regularization #

**Regularizing Neural Networks via Adversarial Model Perturbation**

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

# SLAM #

**Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation**

*  Paper: https://arxiv.org/abs/2105.07593
 *  Code: None

**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

# 长尾分布(Long-Tailed) #

**Adversarial Robustness under Long-Tailed Distribution**

*  Paper(Oral): https://arxiv.org/abs/2104.02703
 *  Code: https://github.com/wutong16/Adversarial\_Long-Tail

**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

# 数据增广(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

# 无监督/自监督(Un/Self-Supervised) #

**Domain-Specific Suppression for Adaptive Object Detection**

*  Paper: https://arxiv.org/abs/2105.03570
 *  Code: None

**A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning**

*  Paper: https://arxiv.org/abs/2104.14558
 *  Code: https://github.com/facebookresearch/SlowFast

**Unsupervised Multi-Source Domain Adaptation for Person Re-Identification**

*  Paper: https://arxiv.org/abs/2104.12961
 *  Code: None

**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

# 半监督学习(Semi-Supervised ) #

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

*  作者单位: 阿里巴巴
 *  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

# 胶囊网络(Capsule Network) #

**Capsule Network is Not More Robust than Convolutional Network**

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

# 图像分类(Image Classification) #

**Correlated Input-Dependent Label Noise in Large-Scale Image Classification**

*  Paper(Oral): https://arxiv.org/abs/2105.10305
 *  Code: https://github.com/google/uncertainty-baselines/tree/master/baselines/imagenet

# 2D目标检测(Object Detection) #

## 2D目标检测 ##

**1. Scaled-YOLOv4: Scaling Cross Stage Partial Network**

*  作者单位: 中央研究院, 英特尔, 静宜大学
 *  Paper: https://arxiv.org/abs/2011.08036
 *  Code: https://github.com/WongKinYiu/ScaledYOLOv4
 *  中文解读: [YOLOv4官方改进版来了！55.8% AP！速度最高达1774 FPS，Scaled-YOLOv4正式开源！][YOLOv4_55.8_ AP_1774 FPS_Scaled-YOLOv4]

**2. You Only Look One-level Feature**

*  作者单位: 中科院, 国科大, 旷视科技
 *  Paper: https://arxiv.org/abs/2103.09460
 *  Code: https://github.com/megvii-model/YOLOF
 *  中文解读: [CVPR 2021 | 没有FPN！中科院&旷视提出YOLOF：你只需看一层特征][CVPR 2021 _ _FPN_YOLOF]

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

*  作者单位: 香港大学, 同济大学, 字节跳动AI Lab, 加利福尼亚大学伯克利分校
 *  Paper: https://arxiv.org/abs/2011.12450
 *  Code: https://github.com/PeizeSun/SparseR-CNN
 *  中文解读: [目标检测新范式！港大同济伯克利提出Sparse R-CNN，代码刚刚开源！][Sparse R-CNN]

**4. End-to-End Object Detection with Fully Convolutional Network**

*  作者单位: 旷视科技, 西安交通大学
 *  Paper: https://arxiv.org/abs/2012.03544
 *  Code: https://github.com/Megvii-BaseDetection/DeFCN

**5. Dynamic Head: Unifying Object Detection Heads with Attentions**

*  作者单位: 微软
 *  Paper: https://arxiv.org/abs/2106.08322
 *  Code: https://github.com/microsoft/DynamicHead
 *  中文解读: [60.6 AP！打破COCO记录！微软提出DyHead：将注意力与目标检测Heads统一][60.6 AP_COCO_DyHead_Heads]

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

*  作者单位: 南京理工大学, Momenta, 南京大学, 清华大学
 *  Paper: https://arxiv.org/abs/2011.12885
 *  Code: https://github.com/implus/GFocalV2
 *  中文解读：[CVPR 2021 | GFLV2：目标检测良心技术，无Cost涨点！][CVPR 2021 _ GFLV2_Cost]

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

*  作者单位: 华南理工大学, 腾讯微信AI
 *  Paper(Oral): https://arxiv.org/abs/2011.09094
 *  Code: https://github.com/dddzg/up-detr
 *  中文解读: [CVPR 2021 Oral | Transformer再发力！华南理工和微信提出UP-DETR：无监督预训练检测器][CVPR 2021 Oral _ Transformer_UP-DETR]

**8. MobileDets: Searching for Object Detection Architectures for Mobile Accelerators**

*  作者单位: 威斯康星大学, 谷歌
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Xiong\_MobileDets\_Searching\_for\_Object\_Detection\_Architectures\_for\_Mobile\_Accelerators\_CVPR\_2021\_paper.pdf
 *  Code: https://github.com/tensorflow/models/tree/master/research/object\_detection

**9. Tracking Pedestrian Heads in Dense Crowd**

*  作者单位: 雷恩第一大学
 *  Homepage: https://project.inria.fr/crowdscience/project/dense-crowd-head-tracking/
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Sundararaman\_Tracking\_Pedestrian\_Heads\_in\_Dense\_Crowd\_CVPR\_2021\_paper.html
 *  Code1: https://github.com/Sentient07/HeadHunter
 *  Code2: https://github.com/Sentient07/HeadHunter%E2%80%93T
 *  Dataset: https://project.inria.fr/crowdscience/project/dense-crowd-head-tracking/

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

*  作者单位: 香港科技大学, 华为诺亚
 *  Paper: https://arxiv.org/abs/2105.12971
 *  Code: None

**11. PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery**

*  作者单位: A\*star, 四川大学, 南洋理工大学
 *  Paper: https://arxiv.org/abs/2105.12990
 *  Code: None

**12. IQDet: Instance-wise Quality Distribution Sampling for Object Detection**

*  作者单位: 旷视科技
 *  Paper: https://arxiv.org/abs/2104.06936
 *  Code: None

**13. Multi-Scale Aligned Distillation for Low-Resolution Detection**

*  作者单位: 香港中文大学, Adobe研究院, 思谋科技
 *  Paper: https://jiaya.me/papers/ms\_align\_distill\_cvpr21.pdf
 *  Code: https://github.com/Jia-Research-Lab/MSAD

**14. Adaptive Class Suppression Loss for Long-Tail Object Detection**

*  作者单位: 中科院, 国科大, ObjectEye, 北京大学, 鹏城实验室, Nexwise
 *  Paper: https://arxiv.org/abs/2104.00885
 *  Code: https://github.com/CASIA-IVA-Lab/ACSL

**15. VarifocalNet: An IoU-aware Dense Object Detector**

*  作者单位: 昆士兰科技大学, 昆士兰大学
 *  Paper(Oral): https://arxiv.org/abs/2008.13367
 *  Code: https://github.com/hyz-xmaster/VarifocalNet

**16. OTA: Optimal Transport Assignment for Object Detection**

*  作者单位: 早稻田大学, 旷视科技
 *  Paper: https://arxiv.org/abs/2103.14259
 *  Code: https://github.com/Megvii-BaseDetection/OTA

**17. Distilling Object Detectors via Decoupled Features**

*  作者单位: 华为诺亚, 悉尼大学
 *  Paper: https://arxiv.org/abs/2103.14475
 *  Code: https://github.com/ggjy/DeFeat.pytorch

**18. Robust and Accurate Object Detection via Adversarial Learning**

*  作者单位: 谷歌, UCLA, UCSC
 *  Paper: https://arxiv.org/abs/2103.13886
 *  Code: None

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

*  作者单位: 北京大学, Anyvision, 石溪大学
 *  Paper: https://arxiv.org/abs/2103.04507
 *  Code: https://github.com/VDIGPKU/OPANAS

**20. Multiple Instance Active Learning for Object Detection**

*  作者单位: 国科大, 华为诺亚, 清华大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan\_Multiple\_Instance\_Active\_Learning\_for\_Object\_Detection\_CVPR\_2021\_paper.pdf
 *  Code: https://github.com/yuantn/MI-AOD

**21. Towards Open World Object Detection**

*  作者单位: 印度理工学院, MBZUAI, 澳大利亚国立大学, 林雪平大学
 *  Paper(Oral): https://arxiv.org/abs/2103.02603
 *  Code: https://github.com/JosephKJ/OWOD

**22. RankDetNet: Delving Into Ranking Constraints for Object Detection**

*  作者单位: 赛灵思
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Liu\_RankDetNet\_Delving\_Into\_Ranking\_Constraints\_for\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

## 旋转目标检测 ##

**23. Dense Label Encoding for Boundary Discontinuity Free Rotation Detection**

*  作者单位: 上海交通大学, 国科大
 *  Paper: https://arxiv.org/abs/2011.09670
 *  Code1: https://github.com/Thinklab-SJTU/DCL\_RetinaNet\_Tensorflow
 *  Code2: https://github.com/yangxue0827/RotationDetection

**24. ReDet: A Rotation-equivariant Detector for Aerial Object Detection**

*  作者单位: 武汉大学
 *  Paper: https://arxiv.org/abs/2103.07733
 *  Code: https://github.com/csuhan/ReDet

**25. Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection**

*  作者单位: 国科大, 清华大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Guo\_Beyond\_Bounding-Box\_Convex-Hull\_Feature\_Adaptation\_for\_Oriented\_and\_Densely\_Packed\_CVPR\_2021\_paper.html
 *  Code: https://github.com/SDL-GuoZonghao/BeyondBoundingBox

## Few-Shot目标检测 ##

**26. Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss**

*  作者单位: 复旦大学, 同济大学, 浙江大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Zhang\_Accurate\_Few-Shot\_Object\_Detection\_With\_Support-Query\_Mutual\_Guidance\_and\_Hybrid\_CVPR\_2021\_paper.html
 *  Code: None

**27. Adaptive Image Transformer for One-Shot Object Detection**

*  作者单位: 中央研究院, 台湾AI Labs
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Chen\_Adaptive\_Image\_Transformer\_for\_One-Shot\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

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

*  作者单位: 北京大学, 北邮
 *  Paper: https://arxiv.org/abs/2103.17115
 *  Code: https://github.com/hzhupku/DCNet

**29. Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection**

*  作者单位: 卡内基梅隆大学(CMU)
 *  Paper: https://arxiv.org/abs/2103.01903
 *  Code: None

**30. FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding**

*  作者单位: 南加利福尼亚大学, 旷视科技
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Sun\_FSCE\_Few-Shot\_Object\_Detection\_via\_Contrastive\_Proposal\_Encoding\_CVPR\_2021\_paper.html
 *  Code: https://github.com/MegviiDetection/FSCE

**31. Hallucination Improves Few-Shot Object Detection**

*  作者单位: 伊利诺伊大学厄巴纳-香槟分校
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Zhang\_Hallucination\_Improves\_Few-Shot\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: https://github.com/pppplin/HallucFsDet

**32. Few-Shot Object Detection via Classification Refinement and Distractor Retreatment**

*  作者单位: 新加坡国立大学, SIMTech
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Li\_Few-Shot\_Object\_Detection\_via\_Classification\_Refinement\_and\_Distractor\_Retreatment\_CVPR\_2021\_paper.html
 *  Code: None

**33. Generalized Few-Shot Object Detection Without Forgetting**

*  作者单位: 旷视科技
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Fan\_Generalized\_Few-Shot\_Object\_Detection\_Without\_Forgetting\_CVPR\_2021\_paper.html
 *  Code: None

**34. Transformation Invariant Few-Shot Object Detection**

*  作者单位: 华为诺亚方舟实验室
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Li\_Transformation\_Invariant\_Few-Shot\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

**35. UniT: Unified Knowledge Transfer for Any-Shot Object Detection and Segmentation**

*  作者单位: 不列颠哥伦比亚大学, Vector AI, CIFAR AI Chair
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Khandelwal\_UniT\_Unified\_Knowledge\_Transfer\_for\_Any-Shot\_Object\_Detection\_and\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: https://github.com/ubc-vision/UniT

**36. Beyond Max-Margin: Class Margin Equilibrium for Few-Shot Object Detection**

*  作者单位: 国科大, 厦门大学, 鹏城实验室
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Li\_Beyond\_Max-Margin\_Class\_Margin\_Equilibrium\_for\_Few-Shot\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: https://github.com/Bohao-Lee/CME

## 半监督目标检测 ##

**37. Points As Queries: Weakly Semi-Supervised Object Detection by Points\]**

*  作者单位: 旷视科技, 复旦大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Chen\_Points\_As\_Queries\_Weakly\_Semi-Supervised\_Object\_Detection\_by\_Points\_CVPR\_2021\_paper.html
 *  Code: None

**38. Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection**

*  作者单位: 清华大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Wang\_Data-Uncertainty\_Guided\_Multi-Phase\_Learning\_for\_Semi-Supervised\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

**39. Positive-Unlabeled Data Purification in the Wild for Object Detection**

*  作者单位: 华为诺亚方舟实验室, 悉尼大学, 北京大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Guo\_Positive-Unlabeled\_Data\_Purification\_in\_the\_Wild\_for\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

**40. Interactive Self-Training With Mean Teachers for Semi-Supervised Object Detection**

*  作者单位: 阿里巴巴, 香港理工大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Yang\_Interactive\_Self-Training\_With\_Mean\_Teachers\_for\_Semi-Supervised\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

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

*  作者单位: 阿里巴巴
 *  Paper: https://arxiv.org/abs/2103.11402
 *  Code: None

**42. Humble Teachers Teach Better Students for Semi-Supervised Object Detection**

*  作者单位: 卡内基梅隆大学(CMU), 亚马逊
 *  Homepage: https://yihet.com/humble-teacher
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Tang\_Humble\_Teachers\_Teach\_Better\_Students\_for\_Semi-Supervised\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: https://github.com/lryta/HumbleTeacher

**43. Interpolation-Based Semi-Supervised Learning for Object Detection**

*  作者单位: 首尔大学, 阿尔托大学等
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Jeong\_Interpolation-Based\_Semi-Supervised\_Learning\_for\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: https://github.com/soo89/ISD-SSD

# 域自适应目标检测 #

**44. Domain-Specific Suppression for Adaptive Object Detection**

*  作者单位: 中科院, 寒武纪, 国科大
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Wang\_Domain-Specific\_Suppression\_for\_Adaptive\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

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

*  作者单位: 约翰斯·霍普金斯大学, 梅赛德斯—奔驰
 *  Paper: https://arxiv.org/abs/2103.04224
 *  Code: None

**46. Unbiased Mean Teacher for Cross-Domain Object Detection**

*  作者单位: 电子科技大学, ETH Zurich
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Deng\_Unbiased\_Mean\_Teacher\_for\_Cross-Domain\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: https://github.com/kinredon/umt

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

*  作者单位: 香港大学, 厦门大学, Deepwise AI Lab
 *  Paper: https://arxiv.org/abs/2103.13757
 *  Code: None

## 自监督目标检测 ##

**48. There Is More Than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking With Sound by Distilling Multimodal Knowledge**

*  作者单位: 弗莱堡大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Valverde\_There\_Is\_More\_Than\_Meets\_the\_Eye\_Self-Supervised\_Multi-Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: http://rl.uni-freiburg.de/research/multimodal-distill

**49. Instance Localization for Self-supervised Detection Pretraining**

*  作者单位: 香港中文大学, 微软亚洲研究院
 *  Paper: https://arxiv.org/abs/2102.08318
 *  Code: https://github.com/limbo0000/InstanceLoc

## 弱监督目标检测 ##

**50. Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection**

*  作者单位: 北航, 鹏城实验室, 商汤科技
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Hou\_Informative\_and\_Consistent\_Correspondence\_Mining\_for\_Cross-Domain\_Weakly\_Supervised\_Object\_CVPR\_2021\_paper.html
 *  Code: None

**51. DAP: Detection-Aware Pre-training with Weak Supervision**

*  作者单位: UIUC, 微软
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Zhong\_DAP\_Detection-Aware\_Pre-Training\_With\_Weak\_Supervision\_CVPR\_2021\_paper.html
 *  Code: None

## 其他 ##

**52. Open-Vocabulary Object Detection Using Captions**

*  作者单位：Snap, 哥伦比亚大学
 *  Paper(Oral): https://openaccess.thecvf.com/content/CVPR2021/html/Zareian\_Open-Vocabulary\_Object\_Detection\_Using\_Captions\_CVPR\_2021\_paper.html
 *  Code: https://github.com/alirezazareian/ovr-cnn

**53. Depth From Camera Motion and Object Detection**

*  作者单位: 密歇根大学, SIAI
 *  Paper: https://arxiv.org/abs/2103.01468
 *  Code: https://github.com/griffbr/ODMD
 *  Dataset: https://github.com/griffbr/ODMD

**54. Unsupervised Object Detection With LIDAR Clues**

*  作者单位: 商汤科技, 国科大, 中科大
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Tian\_Unsupervised\_Object\_Detection\_With\_LIDAR\_Clues\_CVPR\_2021\_paper.html
 *  Code: None

**55. GAIA: A Transfer Learning System of Object Detection That Fits Your Needs**

*  作者单位: 国科大, 北理, 中科院, 商汤科技
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Bu\_GAIA\_A\_Transfer\_Learning\_System\_of\_Object\_Detection\_That\_Fits\_CVPR\_2021\_paper.html
 *  Code: https://github.com/GAIA-vision/GAIA-det

**56. General Instance Distillation for Object Detection**

*  作者单位: 旷视科技, 北航
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Dai\_General\_Instance\_Distillation\_for\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

**57. AQD: Towards Accurate Quantized Object Detection**

*  作者单位: 蒙纳士大学, 阿德莱德大学, 华南理工大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Chen\_AQD\_Towards\_Accurate\_Quantized\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: https://github.com/aim-uofa/model-quantization

**58. Scale-Aware Automatic Augmentation for Object Detection**

*  作者单位: 香港中文大学, 字节跳动AI Lab, 思谋科技
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Chen\_Scale-Aware\_Automatic\_Augmentation\_for\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: https://github.com/Jia-Research-Lab/SA-AutoAug

**59. Equalization Loss v2: A New Gradient Balance Approach for Long-Tailed Object Detection**

*  作者单位: 同济大学, 商汤科技, 清华大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Tan\_Equalization\_Loss\_v2\_A\_New\_Gradient\_Balance\_Approach\_for\_Long-Tailed\_CVPR\_2021\_paper.html
 *  Code: https://github.com/tztztztztz/eqlv2

**60. Class-Aware Robust Adversarial Training for Object Detection**

*  作者单位: 哥伦比亚大学, 中央研究院
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Chen\_Class-Aware\_Robust\_Adversarial\_Training\_for\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

**61. Improved Handling of Motion Blur in Online Object Detection**

*  作者单位: 伦敦大学学院
 *  Homepage: http://visual.cs.ucl.ac.uk/pubs/handlingMotionBlur/
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Sayed\_Improved\_Handling\_of\_Motion\_Blur\_in\_Online\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

**62. Multiple Instance Active Learning for Object Detection**

*  作者单位: 国科大, 华为诺亚
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Yuan\_Multiple\_Instance\_Active\_Learning\_for\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: https://github.com/yuantn/MI-AOD

**63. Neural Auto-Exposure for High-Dynamic Range Object Detection**

*  作者单位: Algolux, 普林斯顿大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Onzon\_Neural\_Auto-Exposure\_for\_High-Dynamic\_Range\_Object\_Detection\_CVPR\_2021\_paper.html
 *  Code: None

**64. Generalizable Pedestrian Detection: The Elephant in the Room**

*  作者单位: IIAI, 阿尔托大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Hasan\_Generalizable\_Pedestrian\_Detection\_The\_Elephant\_in\_the\_Room\_CVPR\_2021\_paper.html
 *  Code: https://github.com/hasanirtiza/Pedestron

**65. Neural Auto-Exposure for High-Dynamic Range Object Detection**

# 单/多目标跟踪(Object Tracking) #

## 单目标跟踪 ##

**LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search**

*  Paper: https://arxiv.org/abs/2104.14545
 *  Code: https://github.com/researchmm/LightTrack

**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

## 多目标跟踪 ##

**Tracking Pedestrian Heads in Dense Crowd**

*  Homepage: https://project.inria.fr/crowdscience/project/dense-crowd-head-tracking/
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Sundararaman\_Tracking\_Pedestrian\_Heads\_in\_Dense\_Crowd\_CVPR\_2021\_paper.html
 *  Code1: https://github.com/Sentient07/HeadHunter
 *  Code2: https://github.com/Sentient07/HeadHunter%E2%80%93T
 *  Dataset: https://project.inria.fr/crowdscience/project/dense-crowd-head-tracking/

**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

# 语义分割(Semantic Segmentation) #

**1. HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation**

*  作者单位: Facebook AI, 巴伊兰大学, 特拉维夫大学
 *  Homepage: https://nirkin.com/hyperseg/
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Nirkin\_HyperSeg\_Patch-Wise\_Hypernetwork\_for\_Real-Time\_Semantic\_Segmentation\_CVPR\_2021\_paper.pdf
 *  Code: https://github.com/YuvalNirkin/hyperseg

**2. Rethinking BiSeNet For Real-time Semantic Segmentation**

*  作者单位: 美团
 *  Paper: https://arxiv.org/abs/2104.13188
 *  Code: https://github.com/MichaelFan01/STDC-Seg

**3. Progressive Semantic Segmentation**

*  作者单位: VinAI Research, VinUniversity, 阿肯色大学, 石溪大学
 *  Paper: https://arxiv.org/abs/2104.03778
 *  Code: https://github.com/VinAIResearch/MagNet

**4. Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers**

*  作者单位: 复旦大学, 牛津大学, 萨里大学, 腾讯优图, Facebook AI
 *  Homepage: https://fudan-zvg.github.io/SETR
 *  Paper: https://arxiv.org/abs/2012.15840
 *  Code: https://github.com/fudan-zvg/SETR

**5. Capturing Omni-Range Context for Omnidirectional Segmentation**

*  作者单位: 卡尔斯鲁厄理工学院, 卡尔·蔡司, 华为
 *  Paper: https://arxiv.org/abs/2103.05687
 *  Code: None

**6. Learning Statistical Texture for Semantic Segmentation**

*  作者单位: 北航, 商汤科技
 *  Paper: https://arxiv.org/abs/2103.04133
 *  Code: None

**7. InverseForm: A Loss Function for Structured Boundary-Aware Segmentation**

*  作者单位: 高通AI研究院
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Borse\_InverseForm\_A\_Loss\_Function\_for\_Structured\_Boundary-Aware\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: None

**8. DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation**

*  作者单位: Joyy Inc, 快手, 北航等
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Zhang\_DCNAS\_Densely\_Connected\_Neural\_Architecture\_Search\_for\_Semantic\_Image\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: None

## 弱监督语义分割 ##

**9. Railroad Is Not a Train: Saliency As Pseudo-Pixel Supervision for Weakly Supervised Semantic Segmentation**

*  作者单位: 延世大学, 成均馆大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Lee\_Railroad\_Is\_Not\_a\_Train\_Saliency\_As\_Pseudo-Pixel\_Supervision\_for\_CVPR\_2021\_paper.html
 *  Code: https://github.com/halbielee/EPS

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

*  作者单位: 延世大学
 *  Homepage: https://cvlab.yonsei.ac.kr/projects/BANA/
 *  Paper: https://arxiv.org/abs/2104.00905
 *  Code: None

**11. Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation**

*  作者单位: 南京理工大学, MBZUAI, 电子科技大学, 阿德莱德大学, 悉尼科技大学
 *  Paper: https://arxiv.org/abs/2103.14581
 *  Code: https://github.com/NUST-Machine-Intelligence-Laboratory/nsrom

**12. Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation**

*  作者单位: 北京理工大学, 美团
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Wu\_Embedded\_Discriminative\_Attention\_Mechanism\_for\_Weakly\_Supervised\_Semantic\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: https://github.com/allenwu97/EDAM

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

*  作者单位: 首尔大学
 *  Paper: https://arxiv.org/abs/2103.08907
 *  Code: https://github.com/jbeomlee93/BBAM

## 半监督语义分割 ##

**14. Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision**

*  作者单位: 北京大学, 微软亚洲研究院
 *  Paper: https://arxiv.org/abs/2106.01226
 *  Code: https://github.com/charlesCXK/TorchSemiSeg

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

*  作者单位: 华为, 大连理工大学, 北京大学
 *  Paper: https://arxiv.org/abs/2103.04705
 *  Code: None

**16. Semi-Supervised Semantic Segmentation With Directional Context-Aware Consistency**

*  作者单位: 香港中文大学, 思谋科技, 牛津大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Lai\_Semi-Supervised\_Semantic\_Segmentation\_With\_Directional\_Context-Aware\_Consistency\_CVPR\_2021\_paper.html
 *  Code: None

**17. Semantic Segmentation With Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization**

*  作者单位: NVIDIA, 多伦多大学, 耶鲁大学, MIT, Vector Institute
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Li\_Semantic\_Segmentation\_With\_Generative\_Models\_Semi-Supervised\_Learning\_and\_Strong\_Out-of-Domain\_CVPR\_2021\_paper.html
 *  Code: https://nv-tlabs.github.io/semanticGAN/

**18. Three Ways To Improve Semantic Segmentation With Self-Supervised Depth Estimation**

*  作者单位: ETH Zurich, 伯恩大学, 鲁汶大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Hoyer\_Three\_Ways\_To\_Improve\_Semantic\_Segmentation\_With\_Self-Supervised\_Depth\_Estimation\_CVPR\_2021\_paper.html
 *  Code: https://github.com/lhoyer/improving\_segmentation\_with\_selfsupervised\_depth

## 域自适应语义分割 ##

**19. Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation**

*  作者单位: ETH Zurich, 鲁汶大学, 电子科技大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Gong\_Cluster\_Split\_Fuse\_and\_Update\_Meta-Learning\_for\_Open\_Compound\_Domain\_CVPR\_2021\_paper.html
 *  Code: None

**20. Source-Free Domain Adaptation for Semantic Segmentation**

*  作者单位: 华东师范大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Liu\_Source-Free\_Domain\_Adaptation\_for\_Semantic\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: None

**21. Uncertainty Reduction for Model Adaptation in Semantic Segmentation**

*  作者单位: Idiap Research Institute, EPFL, 日内瓦大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/S\_Uncertainty\_Reduction\_for\_Model\_Adaptation\_in\_Semantic\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: https://git.io/JthPp

**22. Self-Supervised Augmentation Consistency for Adapting Semantic Segmentation**

*  作者单位: 达姆施塔特工业大学, hessian.AI
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Araslanov\_Self-Supervised\_Augmentation\_Consistency\_for\_Adapting\_Semantic\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: https://github.com/visinf/da-sac

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

*  作者单位: LG AI研究院, KAIST等
 *  Paper: https://arxiv.org/abs/2103.15597
 *  Code: https://github.com/shachoi/RobustNet

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

*  作者单位: 香港大学, 深睿医疗
 *  Paper: https://arxiv.org/abs/2103.13041
 *  Code: None

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

*  作者单位: 香港城市大学, 百度
 *  Paper: https://arxiv.org/abs/2103.05254
 *  Code: https://github.com/cyang-cityu/MetaCorrection

**26. Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation**

*  作者单位: 华为云, 华为诺亚, 大连理工大学
 *  Paper: https://arxiv.org/abs/2103.04717
 *  Code: None

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

*  作者单位: 中国科学技术大学, 微软亚洲研究院
 *  Paper: https://arxiv.org/abs/2101.10979
 *  Code: https://github.com/microsoft/ProDA

**28. DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation**

*  作者单位: 南卡罗来纳大学, 天远视科技
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Wu\_DANNet\_A\_One-Stage\_Domain\_Adaptation\_Network\_for\_Unsupervised\_Nighttime\_Semantic\_CVPR\_2021\_paper.html
 *  Code: https://github.com/W-zx-Y/DANNet

## Few-Shot语义分割 ##

**29. Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation**

*  作者单位: MBZUAI, IIAI, 哈工大
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Xie\_Scale-Aware\_Graph\_Neural\_Network\_for\_Few-Shot\_Semantic\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: None

**30. Anti-Aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation**

*  作者单位: 国科大, 清华大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Liu\_Anti-Aliasing\_Semantic\_Reconstruction\_for\_Few-Shot\_Semantic\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: https://github.com/Bibkiller/ASR

## 无监督语义分割 ##

**31. PiCIE: Unsupervised Semantic Segmentation Using Invariance and Equivariance in Clustering**

*  作者单位: UT-Austin, 康奈尔大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Cho\_PiCIE\_Unsupervised\_Semantic\_Segmentation\_Using\_Invariance\_and\_Equivariance\_in\_Clustering\_CVPR\_2021\_paper.html
 *  Code: https:// github.com/janghyuncho/PiCIE

## 视频语义分割 ##

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

*  作者单位: 浙江大学, 百度, 悉尼科技大学
 *  Homepage: https://www.vspwdataset.com/
 *  Paper: https://www.vspwdataset.com/CVPR2021\_\_miao.pdf
 *  GitHub: https://github.com/sssdddwww2/vspw\_dataset\_download

## 其它 ##

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

*  作者单位: 帕多瓦大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Michieli\_Continual\_Semantic\_Segmentation\_via\_Repulsion-Attraction\_of\_Sparse\_and\_Disentangled\_Latent\_CVPR\_2021\_paper.html
 *  Code: https://lttm.dei.unipd.it/paper\_data/SDR/

**34. Exploit Visual Dependency Relations for Semantic Segmentation**

*  作者单位: 伊利诺伊大学芝加哥分校
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Liu\_Exploit\_Visual\_Dependency\_Relations\_for\_Semantic\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: None

**35. Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs**

*  作者单位: Institute for Infocomm Research, 新加坡国立大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Cai\_Revisiting\_Superpixels\_for\_Active\_Learning\_in\_Semantic\_Segmentation\_With\_Realistic\_CVPR\_2021\_paper.html
 *  Code: None

**36. PLOP: Learning without Forgetting for Continual Semantic Segmentation**

*  作者单位: 索邦大学, Heuritech, Datakalab, Valeo.ai
 *  Paper: https://arxiv.org/abs/2011.11390
 *  Code: https://github.com/arthurdouillard/CVPR2021\_PLOP

**37. 3D-to-2D Distillation for Indoor Scene Parsing**

*  作者单位: 香港中文大学, 香港大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Liu\_3D-to-2D\_Distillation\_for\_Indoor\_Scene\_Parsing\_CVPR\_2021\_paper.html
 *  Code: None

**38. Bidirectional Projection Network for Cross Dimension Scene Understanding**

*  作者单位: 香港中文大学, 牛津大学等
 *  Paper(Oral): https://arxiv.org/abs/2103.14326
 *  Code: https://github.com/wbhu/BPNet

**39. PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation**

*  作者单位: 北京大学, 中科院, 国科大, ETH Zurich, 商汤科技等
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Li\_PointFlow\_Flowing\_Semantics\_Through\_Points\_for\_Aerial\_Image\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: https://github.com/lxtGH/PFSegNets

# 实例分割(Instance Segmentation) #

**DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation**

*  Paper: https://arxiv.org/abs/2011.09876
 *  Code: https://github.com/aliyun/DCT-Mask

**Incremental Few-Shot Instance Segmentation**

*  Paper: https://arxiv.org/abs/2105.05312
 *  Code: https://github.com/danganea/iMTFA

**A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation**

*  Paper: https://arxiv.org/abs/2105.03186
 *  Code: None

**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

## 视频实例分割 ##

**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) #

**ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation**

*  Paper: https://arxiv.org/abs/2012.05258
 *  Code: https://github.com/joe-siyuan-qiao/ViP-DeepLab
 *  Dataset: https://github.com/joe-siyuan-qiao/ViP-DeepLab

**Part-aware Panoptic Segmentation**

*  Paper: https://arxiv.org/abs/2106.06351
 *  Code: https://github.com/tue-mps/panoptic\_parts
 *  Dataset: https://github.com/tue-mps/panoptic\_parts

**Exemplar-Based Open-Set Panoptic Segmentation Network**

*  Homepage: https://cv.snu.ac.kr/research/EOPSN/
 *  Paper: https://arxiv.org/abs/2105.08336
 *  Code: https://github.com/jd730/EOPSN

**MaX-DeepLab: End-to-End Panoptic Segmentation With Mask Transformers**

*  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Wang\_MaX-DeepLab\_End-to-End\_Panoptic\_Segmentation\_With\_Mask\_Transformers\_CVPR\_2021\_paper.html
 *  Code: None

**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

# 医学图像分割 #

**1. Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling**

*  作者单位: 腾讯天衍实验室, 北京同仁医院
 *  Paper(Best Paper Candidate): https://openaccess.thecvf.com/content/CVPR2021/html/Ji\_Learning\_Calibrated\_Medical\_Image\_Segmentation\_via\_Multi-Rater\_Agreement\_Modeling\_CVPR\_2021\_paper.html
 *  Code: https://github.com/jiwei0921/MRNet/

**2. Every Annotation Counts: Multi-Label Deep Supervision for Medical Image Segmentation**

*  作者单位: 卡尔斯鲁厄理工学院, 卡尔·蔡司等
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Reiss\_Every\_Annotation\_Counts\_Multi-Label\_Deep\_Supervision\_for\_Medical\_Image\_Segmentation\_CVPR\_2021\_paper.html
 *  Code: None

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

*  作者单位: 香港中文大学, 香港理工大学
 *  Paper: https://arxiv.org/abs/2103.06030
 *  Code: https://github.com/liuquande/FedDG-ELCFS

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

*  作者单位: 约翰斯·霍普金斯大大学, NVIDIA
 *  Paper(Oral): https://arxiv.org/abs/2103.15954
 *  Code: None

**5. DARCNN: Domain Adaptive Region-Based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images**

*  作者单位: 斯坦福大学
 *  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Hsu\_DARCNN\_Domain\_Adaptive\_Region-Based\_Convolutional\_Neural\_Network\_for\_Unsupervised\_Instance\_CVPR\_2021\_paper.html
 *  Code: None

# 视频目标分割(Video-Object-Segmentation) #

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

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

**SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation**

*  Paper(Oral): https://arxiv.org/abs/2101.08833
 *  Code: https://github.com/dukebw/SSTVOS

# 交互式视频目标分割(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

# 显著性检测(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

# 伪装物体检测(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

# 协同显著性检测(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

# 协同显著性检测(Image Matting) #

**Semantic Image Matting**

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

# 行人重识别(Person Re-identification) #

**Generalizable Person Re-identification with Relevance-aware Mixture of Experts**

*  Paper: https://arxiv.org/abs/2105.09156
 *  Code: None

**Unsupervised Multi-Source Domain Adaptation for Person Re-Identification**

*  Paper: https://arxiv.org/abs/2104.12961
 *  Code: None

**Combined Depth Space based Architecture Search For Person Re-identification**

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

# 行人搜索(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）的行人搜索框架 | CVPR 2021][Anchor-Free_ _ CVPR 2021]

# 视频理解/行为识别(Video Understanding) #

**Temporal-Relational CrossTransformers for Few-Shot Action Recognition**

*  Paper: https://arxiv.org/abs/2101.06184
 *  Code: https://github.com/tobyperrett/trx

**FrameExit: Conditional Early Exiting for Efficient Video Recognition**

*  Paper(Oral): https://arxiv.org/abs/2104.13400
 *  Code: None

**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][CVPR 2021 _ TCANet]

**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

# 人脸识别(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

# 人脸检测(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

# 人脸活体检测(Face Anti-Spoofing) #

**Cross Modal Focal Loss for RGBD Face Anti-Spoofing**

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

# Deepfake检测(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

# 人脸年龄估计(Age Estimation) #

**Continuous Face Aging via Self-estimated Residual Age Embedding**

*  Paper: https://arxiv.org/abs/2105.00020
 *  Code: None

**PML: Progressive Margin Loss for Long-tailed Age Classification**

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

# 人脸表情识别(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人体姿态估计(2D/3D Human Pose Estimation) #

## 2D 人体姿态估计 ##

**ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search**

*  Paper: ttps://arxiv.org/abs/2105.10154
 *  Code: None

**When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks**

*  Paper: https://arxiv.org/abs/2105.06152
 *  Code: None

**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 人体姿态估计 ##

**End-to-End Human Pose and Mesh Reconstruction with Transformers**

*  Paper: https://arxiv.org/abs/2012.09760
 *  Code: https://github.com/microsoft/MeshTransformer

**PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation**

*  Paper(Oral): https://arxiv.org/abs/2105.02465
 *  Code: https://github.com/jfzhang95/PoseAug

**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

# 动物姿态估计(Animal Pose Estimation) #

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

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

# 手部姿态估计(Hand Pose Estimation) #

**Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time**

*  Homepage: https://stevenlsw.github.io/Semi-Hand-Object/
 *  Paper: https://arxiv.org/abs/2106.05266
 *  Code: https://github.com/stevenlsw/Semi-Hand-Object

# 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

# 场景文本检测(Scene Text Detection) #

**Fourier Contour Embedding for Arbitrary-Shaped Text Detection**

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

# 场景文本识别(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

# 图像压缩 #

**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

# 模型压缩/剪枝/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

## 模型剪枝 ##

**Dynamic Slimmable Network**

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

## 模型量化 ##

**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

# 超分辨率(Super-Resolution) #

**Image Super-Resolution with Non-Local Sparse Attention**

*  Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Mei\_Image\_Super-Resolution\_With\_Non-Local\_Sparse\_Attention\_CVPR\_2021\_paper.pdf
 *  Code: https://github.com/HarukiYqM/Non-Local-Sparse-Attention

**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

## 视频超分辨率 ##

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

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

# 图像恢复(Image Restoration) #

**Multi-Stage Progressive Image Restoration**

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

# 图像补全(Image Inpainting) #

**PD-GAN: Probabilistic Diverse GAN for Image Inpainting**

*  Paper: https://arxiv.org/abs/2105.02201
 *  Code: https://github.com/KumapowerLIU/PD-GAN

**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

# 图像编辑(Image Editing) #

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

*  Paper: https://arxiv.org/abs/2104.14754
 *  Code: https://github.com/naver-ai/StyleMapGAN
 *  Demo Video: https://youtu.be/qCapNyRA\_Ng

**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

# 图像描述(Image Captioning) #

**Towards Accurate Text-based Image Captioning with Content Diversity Exploration**

*  Paper: https://arxiv.org/abs/2105.03236
 *  Code: None

# 字体生成(Font Generation) #

**DG-Font: Deformable Generative Networks for Unsupervised Font Generation**

*  Paper: https://arxiv.org/abs/2104.03064
 *  Code: https://github.com/ecnuycxie/DG-Font

# 图像匹配(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

# 图像融合(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点云分类(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目标检测(3D Object Detection) #

**3D-MAN: 3D Multi-frame Attention Network for Object Detection**

*  Paper: https://arxiv.org/abs/2103.16054
 *  Code: None

**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语义分割(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目标跟踪(3D Object Trancking) #

**Center-based 3D Object Detection and Tracking**

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

# 3D点云配准(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点云补全(3D Point Cloud Completion) #

**Unsupervised 3D Shape Completion through GAN Inversion**

*  Homepage: https://junzhezhang.github.io/projects/ShapeInversion/
 *  Paper: https://arxiv.org/abs/2104.13366
 *  Code: https://github.com/junzhezhang/shape-inversion

**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) #

**Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection**

*  Paper: http://arxiv.org/abs/2106.07852
 *  Code: https://github.com/TencentYoutuResearch/3DFaceReconstruction-LAP

**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位姿估计(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

# 相机姿态估计 #

**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

# 深度估计(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

# 立体匹配(Stereo Matching) #

**A Decomposition Model for Stereo Matching**

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

# 光流估计(Flow Estimation) #

**Self-Supervised Multi-Frame Monocular Scene Flow**

*  Paper: https://arxiv.org/abs/2105.02216
 *  Code: https://github.com/visinf/multi-mono-sf

**RAFT-3D: Scene Flow using Rigid-Motion Embeddings**

*  Paper: https://arxiv.org/abs/2012.00726v1
 *  Code: None

**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

# 车道线检测(Lane Detection) #

**Focus on Local: Detecting Lane Marker from Bottom Up via Key Point**

*  Paper: https://arxiv.org/abs/2105.13680
 *  Code: None

**Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection**

*  Paper: https://arxiv.org/abs/2010.12035
 *  Code: https://github.com/lucastabelini/LaneATT

# 轨迹预测(Trajectory Prediction) #

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

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

# 人群计数(Crowd Counting) #

**Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark**

*  Paper: https://arxiv.org/abs/2105.02440
 *  Code: https://github.com/VisDrone/DroneCrowd
 *  Dataset: https://github.com/VisDrone/DroneCrowd

# 对抗样本(Adversarial Examples) #

**Enhancing the Transferability of Adversarial Attacks through Variance Tuning**

*  Paper: https://arxiv.org/abs/2103.15571
 *  Code: https://github.com/JHL-HUST/VT

**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

# 图像检索(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

# 视频检索(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**

# 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

# 视频插帧(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

# 图像合成(Image Synthesis) #

**GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields**

**Taming Transformers for High-Resolution Image Synthesis**

*  Homepage: https://compvis.github.io/taming-transformers/
 *  Paper(Oral): https://arxiv.org/abs/2012.09841
 *  Code: https://github.com/CompVis/taming-transformers

# 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

# 风格迁移(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) #

**LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity**

*  Paper: None
 *  Code: None

**Variational Transformer Networks for Layout Generation**

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

# Domain Generalization #

**Generalizable Person Re-identification with Relevance-aware Mixture of Experts**

*  Paper: https://arxiv.org/abs/2105.09156
 *  Code: None

**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 #

**Towards Open World Object Detection**

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

**Exemplar-Based Open-Set Panoptic Segmentation Network**

*  Homepage: https://cv.snu.ac.kr/research/EOPSN/
 *  Paper: https://arxiv.org/abs/2105.08336
 *  Code: https://github.com/jd730/EOPSN

**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

# "人-物"交互(HOI)检测 #

**HOTR: End-to-End Human-Object Interaction Detection with Transformers**

*  Paper: https://arxiv.org/abs/2104.13682
 *  Code: None

**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

# 标签噪声(Label Noise) #

**A Second-Order Approach to Learning with Instance-Dependent Label Noise**

*  Paper(Oral): https://arxiv.org/abs/2012.11854
 *  Code: https://github.com/UCSC-REAL/CAL

# 视频稳像(Video Stabilization) #

**Real-Time Selfie Video Stabilization**

*  Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Yu\_Real-Time\_Selfie\_Video\_Stabilization\_CVPR\_2021\_paper.pdf
 *  Code: https://github.com/jiy173/selfievideostabilization

# 数据集(Datasets) #

**Tracking Pedestrian Heads in Dense Crowd**

**Part-aware Panoptic Segmentation**

*  Paper: https://arxiv.org/abs/2106.06351
 *  Code: https://github.com/tue-mps/panoptic\_parts
 *  Dataset: https://github.com/tue-mps/panoptic\_parts

**Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos**

*  Homepage: https://www.yasamin.page/hdnet\_tiktok
 *  Paper(Oral): https://arxiv.org/abs/2103.03319
 *  Code: https://github.com/yasaminjafarian/HDNet\_TikTok
 *  Dataset: https://www.yasamin.page/hdnet\_tiktok\#h.jr9ifesshn7v

**High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network**

*  Paper: https://arxiv.org/abs/2105.09188
 *  Code: https://github.com/csjliang/LPTN
 *  Dataset: https://github.com/csjliang/LPTN

**Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark**

*  Paper: https://arxiv.org/abs/2105.02440
 *  Code: https://github.com/VisDrone/DroneCrowd
 *  Dataset: https://github.com/VisDrone/DroneCrowd

**Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets**

*  Homepage: https://fidler-lab.github.io/efficient-annotation-cookbook/
 *  Paper(Oral): https://arxiv.org/abs/2104.12690
 *  Code: https://github.com/fidler-lab/efficient-annotation-cookbook

论文下载链接：

**ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation**

*  Paper: https://arxiv.org/abs/2012.05258
 *  Code: https://github.com/joe-siyuan-qiao/ViP-DeepLab
 *  Dataset: https://github.com/joe-siyuan-qiao/ViP-DeepLab

**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**

**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

# 其他(Others) #

**Fast and Accurate Model Scaling**

*  Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Dollar\_Fast\_and\_Accurate\_Model\_Scaling\_CVPR\_2021\_paper.html
 *  Code: https://github.com/facebookresearch/pycls

**Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos**

**Omnimatte: Associating Objects and Their Effects in Video**

*  Homepage: https://omnimatte.github.io/
 *  Paper(Oral): https://arxiv.org/abs/2105.06993
 *  Code: https://omnimatte.github.io/\#code

**Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets**

*  Homepage: https://fidler-lab.github.io/efficient-annotation-cookbook/
 *  Paper(Oral): https://arxiv.org/abs/2104.12690
 *  Code: https://github.com/fidler-lab/efficient-annotation-cookbook

**Motion Representations for Articulated Animation**

*  Paper: https://arxiv.org/abs/2104.11280
 *  Code: https://github.com/snap-research/articulated-animation

**Deep Lucas-Kanade Homography for Multimodal Image Alignment**

*  Paper: https://arxiv.org/abs/2104.11693
 *  Code: https://github.com/placeforyiming/CVPR21-Deep-Lucas-Kanade-Homography

**Skip-Convolutions for Efficient Video Processing**

*  Paper: https://arxiv.org/abs/2104.11487
 *  Code: None

**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篇论文入选CVPR 2021][20_CVPR 2021]
 *  [MePro团队三篇论文被CVPR 2021接收][MePro_CVPR 2021]

# 不确定中没中(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

[https_github.com_amusi_CVPR2021-Papers-with-Code]: https://github.com/amusi/CVPR2021-Papers-with-Code
[YOLOv4_55.8_ AP_1774 FPS_Scaled-YOLOv4]: https://mp.weixin.qq.com/s/AcrJPNoAVhn8cGBUGK7ekA
[CVPR 2021 _ _FPN_YOLOF]: https://mp.weixin.qq.com/s/EJqAG1gTVaP2icI6QL742A
[Sparse R-CNN]: https://mp.weixin.qq.com/s/P2Zgh1wTqf8L2976El5nfQ
[60.6 AP_COCO_DyHead_Heads]: https://mp.weixin.qq.com/s/uYPUqVXwNau71VAYW3bYIA
[CVPR 2021 _ GFLV2_Cost]: https://mp.weixin.qq.com/s/JB7k3NwXU-cDueg6w9mghQ
[CVPR 2021 Oral _ Transformer_UP-DETR]: https://mp.weixin.qq.com/s/Hprp7B16SGFhVEKXfKiRBQ
[Anchor-Free_ _ CVPR 2021]: https://mp.weixin.qq.com/s/iqJkgp0JBanmeBPyHUkb-A
[CVPR 2021 _ TCANet]: https://mp.weixin.qq.com/s/UOWMfpTljkyZznHtpkQBhA
[20_CVPR 2021]: https://mp.weixin.qq.com/s/McAtOVh0osWZ3uppEoHC8A
[MePro_CVPR 2021]: https://mp.weixin.qq.com/s/GD5Zb6u_MQ8GZIAGeCGo3Q