# CVPR2021-Papers-with-Code **Repository Path**: PythonIoT/CVPR2021-Papers-with-Code ## Basic Information - **Project Name**: CVPR2021-Papers-with-Code - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-09-21 - **Last Updated**: 2021-09-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # CVPR 2021 论文和开源项目合集(Papers with Code) [CVPR 2021](http://cvpr2021.thecvf.com/) 论文和开源项目合集(papers with code)！ CVPR 2021 收录列表：http://cvpr2021.thecvf.com/sites/default/files/2021-03/accepted_paper_ids.txt > 注1：欢迎各位大佬提交issue，分享CVPR 2021论文和开源项目！ > > 注2：关于往年CV顶会论文以及其他优质CV论文和大盘点，详见： https://github.com/amusi/daily-paper-computer-vision 如果你想了解最新最优质的的CV论文、开源项目和学习资料，欢迎扫码加入【CVer学术交流群】！互相学习，一起进步~ ![](CVer学术交流群.png) ## 【CVPR 2021 论文开源目录】 - [Best Paper](#Best-Paper) - [Backbone](#Backbone) - [NAS](#NAS) - [GAN](#GAN) - [VAE](#VAE) - [Visual Transformer](#Visual-Transformer) - [Regularization](#Regularization) - [SLAM](#SLAM) - [长尾分布(Long-Tailed)](#Long-Tailed) - [数据增广(Data Augmentation)](#DA) - [无监督/自监督(Self-Supervised)](#Un/Self-Supervised) - [半监督(Semi-Supervised)](#Semi-Supervised) - [胶囊网络(Capsule Network)](#Capsule-Network) - [图像分类(Image Classification](#Image-Classification) - [2D目标检测(Object Detection)](#Object-Detection) - [单/多目标跟踪(Object Tracking)](#Object-Tracking) - [语义分割(Semantic Segmentation)](#Semantic-Segmentation) - [实例分割(Instance Segmentation)](#Instance-Segmentation) - [全景分割(Panoptic Segmentation)](#Panoptic-Segmentation) - [医学图像分割(Medical Image Segmentation)](#Medical-Image-Segmentation) - [视频目标分割(Video-Object-Segmentation)](#VOS) - [交互式视频目标分割(Interactive-Video-Object-Segmentation)](#IVOS) - [显著性检测(Saliency Detection)](#Saliency-Detection) - [伪装物体检测(Camouflaged Object Detection)](#Camouflaged-Object-Detection) - [协同显著性检测(Co-Salient Object Detection)](#CoSOD) - [图像抠图(Image Matting)](#Matting) - [行人重识别(Person Re-identification)](#Re-ID) - [行人搜索(Person Search)](#Person-Search) - [视频理解/行为识别(Video Understanding)](#Video-Understanding) - [人脸识别(Face Recognition)](#Face-Recognition) - [人脸检测(Face Detection)](#Face-Detection) - [人脸活体检测(Face Anti-Spoofing)](#Face-Anti-Spoofing) - [Deepfake检测(Deepfake Detection)](#Deepfake-Detection) - [人脸年龄估计(Age-Estimation)](#Age-Estimation) - [人脸表情识别(Facial-Expression-Recognition)](#FER) - [Deepfakes](#Deepfakes) - [人体解析(Human Parsing)](#Human-Parsing) - [2D/3D人体姿态估计(2D/3D Human Pose Estimation)](#Human-Pose-Estimation) - [动物姿态估计(Animal Pose Estimation)](#Animal-Pose-Estimation) - [手部姿态估计(Hand Pose Estimation)](#Hand-Pose-Estimation) - [Human Volumetric Capture](#Human-Volumetric-Capture) - [场景文本识别(Scene Text Recognition)](#Scene-Text-Recognition) - [图像压缩(Image Compression)](#Image-Compression) - [模型压缩/剪枝/量化](#Model-Compression) - [知识蒸馏(Knowledge Distillation)](#KD) - [超分辨率(Super-Resolution)](#Super-Resolution) - [去雾(Dehazing)](#Dehazing) - [图像恢复(Image Restoration)](#Image-Restoration) - [图像补全(Image Inpainting)](#Image-Inpainting) - [图像编辑(Image Editing)](#Image-Editing) - [图像描述(Image Captioning)](#Image-Captioning) - [字体生成(Font Generation)](#Font-Generation) - [图像匹配(Image Matching)](#Image-Matching) - [图像融合(Image Blending)](#Image-Blending) - [反光去除(Reflection Removal)](#Reflection-Removal) - [3D点云分类(3D Point Clouds Classification)](#3D-C) - [3D目标检测(3D Object Detection)](#3D-Object-Detection) - [3D语义分割(3D Semantic Segmentation)](#3D-Semantic-Segmentation) - [3D全景分割(3D Panoptic Segmentation)](#3D-Panoptic-Segmentation) - [3D目标跟踪(3D Object Tracking)](#3D-Object-Tracking) - [3D点云配准(3D Point Cloud Registration)](#3D-PointCloud-Registration) - [3D点云补全(3D-Point-Cloud-Completion)](#3D-Point-Cloud-Completion) - [3D重建(3D Reconstruction)](#3D-Reconstruction) - [6D位姿估计(6D Pose Estimation)](#6D-Pose-Estimation) - [相机姿态估计(Camera Pose Estimation)](#Camera-Pose-Estimation) - [深度估计(Depth Estimation)](#Depth-Estimation) - [立体匹配(Stereo Matching)](#Stereo-Matching) - [光流估计(Flow Estimation)](#Flow-Estimation) - [车道线检测(Lane Detection)](#Lane-Detection) - [轨迹预测(Trajectory Prediction)](#Trajectory-Prediction) - [人群计数(Crowd Counting)](#Crowd-Counting) - [对抗样本(Adversarial-Examples)](#AE) - [图像检索(Image Retrieval)](#Image-Retrieval) - [视频检索(Video Retrieval)](#Video-Retrieval) - [跨模态检索(Cross-modal Retrieval)](#Cross-modal-Retrieval) - [Zero-Shot Learning](#Zero-Shot-Learning) - [联邦学习(Federated Learning)](#Federated-Learning) - [视频插帧(Video Frame Interpolation)](#Video-Frame-Interpolation) - [视觉推理(Visual Reasoning)](#Visual-Reasoning) - [图像合成(Image Synthesis)](#Image-Synthesis) - [视图合成(Visual Synthesis)](#Visual-Synthesis) - [风格迁移(Style Transfer)](#Style-Transfer) - [布局生成(Layout Generation)](#Layout-Generation) - [Domain Generalization](#Domain-Generalization) - [Domain Adaptation](#Domain-Adaptation) - [Open-Set](#Open-Set) - [Adversarial Attack](#Adversarial-Attack) - ["人-物"交互(HOI)检测](#HOI) - [阴影去除(Shadow Removal)](#Shadow-Removal) - [虚拟试衣(Virtual Try-On)](#Virtual-Try-On) - [标签噪声(Label Noise)](#Label-Noise) - [视频稳像(Video Stabilization)](#Video-Stabilization) - [数据集(Datasets)](#Datasets) - [其他(Others)](#Others) - [待添加(TODO)](#TO-DO) - [不确定中没中(Not Sure)](#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！](https://mp.weixin.qq.com/s/AcrJPNoAVhn8cGBUGK7ekA) **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](https://mp.weixin.qq.com/s/EJqAG1gTVaP2icI6QL742A) **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！](https://mp.weixin.qq.com/s/P2Zgh1wTqf8L2976El5nfQ) **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](https://mp.weixin.qq.com/s/uYPUqVXwNau71VAYW3bYIA) **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！](https://mp.weixin.qq.com/s/JB7k3NwXU-cDueg6w9mghQ) **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](https://mp.weixin.qq.com/s/Hprp7B16SGFhVEKXfKiRBQ) **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** - 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 # 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](https://mp.weixin.qq.com/s/iqJkgp0JBanmeBPyHUkb-A) # 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](https://mp.weixin.qq.com/s/UOWMfpTljkyZznHtpkQBhA) **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** - Paper: https://www.amazon.science/publications/revamping-cross-modal-recipe-retrieval-with-hierarchical-transformers-and-self-supervised-learning - Code: https://github.com/amzn/image-to-recipe-transformers # Zero-Shot Learning **Counterfactual Zero-Shot and Open-Set Visual Recognition** - Paper: https://arxiv.org/abs/2103.00887 - Code: https://github.com/yue-zhongqi/gcm-cf # 联邦学习(Federated Learning) **FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space** - Paper: https://arxiv.org/abs/2103.06030 - Code: https://github.com/liuquande/FedDG-ELCFS # 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** - 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 **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 **Generalization on Unseen Domains via Inference-time Label-Preserving Target Projections** - Paper(Oral): https://openaccess.thecvf.com/content/CVPR2021/papers/Pandey_Generalization_on_Unseen_Domains_via_Inference-Time_Label-Preserving_Target_Projections_CVPR_2021_paper.pdf - Code: https://github.com/VSumanth99/InferenceTimeDG **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** - 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/ **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** - Homepage: https://github.com/QingyongHu/SensatUrban - Paper: http://arxiv.org/abs/2009.03137 - Code: https://github.com/QingyongHu/SensatUrban - Dataset: https://github.com/QingyongHu/SensatUrban **When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework** - Paper(Oral): https://arxiv.org/abs/2103.01520 - Code: https://github.com/Hzzone/MTLFace - Dataset: https://github.com/Hzzone/MTLFace **Depth from Camera Motion and Object Detection** - Paper: https://arxiv.org/abs/2103.01468 - Code: https://github.com/griffbr/ODMD - Dataset: https://github.com/griffbr/ODMD **There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge** - Homepage: http://rl.uni-freiburg.de/research/multimodal-distill - Paper: https://arxiv.org/abs/2103.01353 - Code: http://rl.uni-freiburg.de/research/multimodal-distill **Scan2Cap: Context-aware Dense Captioning in RGB-D Scans** - Paper: https://arxiv.org/abs/2012.02206 - Code: https://github.com/daveredrum/Scan2Cap - Dataset: https://github.com/daveredrum/ScanRefer **There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge** - Paper: https://arxiv.org/abs/2103.01353 - Code: http://rl.uni-freiburg.de/research/multimodal-distill - Dataset: http://rl.uni-freiburg.de/research/multimodal-distill # 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** - 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 **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](https://mp.weixin.qq.com/s/McAtOVh0osWZ3uppEoHC8A) - [MePro团队三篇论文被CVPR 2021接收](https://mp.weixin.qq.com/s/GD5Zb6u_MQ8GZIAGeCGo3Q) # 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