CVPR 2021 论文和开源项目合集

Posted 2021-08-03 AI浩

CVPR 2021 论文和开源项目合集(Papers with Code)

地址：https://github.com/amusi/CVPR2021-Papers-with-Code

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

Best Paper

GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields

• Homepage: https://m-niemeyer.github.io/project-pages/giraffe/index.html

• Paper(Oral): https://arxiv.org/abs/2011.12100

• Code: https://github.com/autonomousvision/giraffe

• Demo: http://www.youtube.com/watch?v=fIaDXC-qRSg&vq=hd1080&autoplay=1

Backbone

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

• Paper(Oral): https://arxiv.org/abs/2106.06560

• Code: https://github.com/dingmyu/HR-NAS

BCNet: Searching for Network Width with Bilaterally Coupled Network

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

Decoupled Dynamic Filter Networks

• Homepage: https://thefoxofsky.github.io/project_pages/ddf
• Paper: https://arxiv.org/abs/2104.14107
• Code: https://github.com/thefoxofsky/DDF

Lite-HRNet: A Lightweight High-Resolution Network

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

CondenseNet V2: Sparse Feature Reactivation for Deep Networks

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

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

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

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

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

Scaling Local Self-Attention For Parameter Efficient Visual Backbones

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

• Code: None

ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network

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

Involution: Inverting the Inherence of Convolution for Visual Recognition

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

Coordinate Attention for Efficient Mobile Network Design

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

Inception Convolution with Efficient Dilation Search

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

RepVGG: Making VGG-style ConvNets Great Again

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

NAS

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

• Paper(Oral): https://arxiv.org/abs/2106.06560

• Code: https://github.com/dingmyu/HR-NAS

BCNet: Searching for Network Width with Bilaterally Coupled Network

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

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search

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

Combined Depth Space based Architecture Search For Person Re-identification

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

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

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

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

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

Neural Architecture Search with Random Labels

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

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

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

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

• Paper: https://arxiv.org/abs/2105.12971
• Code: None

Prioritized Architecture Sampling with Monto-Carlo Tree Search

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

Contrastive Neural Architecture Search with Neural Architecture Comparators

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

AttentiveNAS: Improving Neural Architecture Search via Attentive

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

ReNAS: Relativistic Evaluation of Neural Architecture Search

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

HourNAS: Extremely Fast Neural Architecture

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

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

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

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

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

Inception Convolution with Efficient Dilation Search

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

GAN

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

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

DG-Font: Deformable Generative Networks for Unsupervised Font Generation

• Paper: https://arxiv.org/abs/2104.03064

• Code: https://github.com/ecnuycxie/DG-Font

PD-GAN: Probabilistic Diverse GAN for Image Inpainting

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

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

• Paper: https://arxiv.org/abs/2104.14754
• Code: https://github.com/naver-ai/StyleMapGAN
• Demo Video: https://youtu.be/qCapNyRA_Ng

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

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

Regularizing Generative Adversarial Networks under Limited Data

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

Towards Real-World Blind Face Restoration with Generative Facial Prior

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

TediGAN: Text-Guided Diverse Image Generation and Manipulation

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

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

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

Generative Hierarchical Features from Synthesizing Image

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

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

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

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

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

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

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

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

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

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

• Code: None

DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

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

Diverse Semantic Image Synthesis via Probability Distribution Modeling

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

LOHO: Latent Optimization of Hairstyles via Orthogonalization

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

PISE: Person Image Synthesis and Editing with Decoupled GAN

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

DeFLOCNet: Deep Image Editing via Flexible Low-level Controls

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

PD-GAN: Probabilistic Diverse GAN for Image Inpainting

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

Efficient Conditional GAN Transfer with Knowledge Propagation across Classes

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

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

• Paper: None
• Code: None

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

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

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

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

A 3D GAN for Improved Large-pose Facial Recognition

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

HumanGAN: A Generative Model of Humans Images

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

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

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

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

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

Training Generative Adversarial Networks in One Stage

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

Closed-Form Factorization of Latent Semantics in GANs

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

Anycost GANs for Interactive Image Synthesis and Editing

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

Image-to-image Translation via Hierarchical Style Disentanglement

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

VAE

Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders

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

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

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

Visual Transformer

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

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

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

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

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

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

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

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

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

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

6. Pose Recognition with Cascade Transformers

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

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

7. Variational Transformer Networks for Layout Generation

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

8. LoFTR: Detector-Free Local Feature Matching with Transformers

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

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

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

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

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

11. Transformer Tracking

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

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

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

13. MIST: Multiple Instance Spatial Transformer

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

14. Multimodal Motion Prediction with Stacked Transformers

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

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

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

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

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

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

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

17. Pre-Trained Image Processing Transformer

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

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

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

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

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

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

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

21. Transformer Interpretability Beyond Attention Visualization

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

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

• Paper: None
• Code: None

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

• Paper: None
• Code: None

24. Line Segment Detection Using Transformers without Edges

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

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

• Paper: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_MaX-DeepLab_End-to-End_Panoptic_Segmentation_With_Mask_Transformers_CVPR_2021_paper.html
• Code: None

26. SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

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

27. Facial Action Unit Detection With Transformers

• Paper: None
• Code: None

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

• Paper: None
• Code: None

29. Lesion-Aware Transformers for Diabetic Retinopathy Grading

• Paper: None
• Code: None

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

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

31. Adaptive Image Transformer for One-Shot Object Detection

• Paper: None
• Code: None

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

• Paper: None
• Code: None

33. Taming Transformers for High-Resolution Image Synthesis

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

34. Self-Supervised Video Hashing via Bidirectional Transformers

• Paper: None
• Code: None

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

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

36. Gaussian Context Transformer

• Paper: None
• Code: None

37. General Multi-Label Image Classification With Transformers

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

38. Bottleneck Transformers for Visual Recognition

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

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

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

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

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

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

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

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

• Paper: None
• Code: None

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

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

• Code: None

Regularization

Regularizing Neural Networks via Adversarial Model Perturbation

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

SLAM

Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation

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

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

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

长尾分布(Long-Tailed)

Adversarial Robustness under Long-Tailed Distribution

• Paper(Oral): https://arxiv.org/abs/2104.02703
• Code: https://github.com/wutong16/Adversarial_Long-Tail

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

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

Adaptive Class Suppression Loss for Long-Tail Object Detection

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

Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification

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

数据增广(Data Augmentation)

Scale-aware Automatic Augmentation for Object Detection

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

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

无监督/自监督(Un/Self-Supervised)

Domain-Specific Suppression for Adaptive Object Detection

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

A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning

• Paper: https://arxiv.org/abs/2104.14558

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

Unsupervised Multi-Source Domain Adaptation for Person Re-Identification

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

Self-supervised Video Representation Learning by Context and Motion Decoupling

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

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

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

Spatially Consistent Representation Learning

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

VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples

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

Exploring Simple Siamese Representation Learning

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

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

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

半监督学习(Semi-Supervised )

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

• 作者单位: 阿里巴巴

• Paper: https://arxiv.org/abs/2103.11402

• Code: None

Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

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

胶囊网络(Capsule Network)

Capsule Network is Not More Robust than Convolutional Network

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

图像分类(Image Classification)

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

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

2D目标检测(Object Detection)

2D目标检测

1. Scaled-YOLOv4: Scaling Cross Stage Partial Network

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

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

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！

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

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！

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

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: http

  以上是关于CVPR 2021 论文和开源项目合集的主要内容，如果未能解决你的问题，请参考以下文章

  CVPR2020 论文和代码合集

  精选CVPR开源项目学习资源汇总

  CVPR2021 论文合集下载 论文分类汇总

  CVPR2021 论文合集下载 论文分类汇总

  CVPR2021 论文合集下载 论文大全下载 论文分类汇总

  CVPR2021 论文合集下载 论文大全下载 论文分类汇总