CVPR 2021 论文和开源项目合集(Papers with Code)

向AI转型的程序员都关注了这个号👇👇👇

人工智能大数据与深度学习 公众号：datayx

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: None

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): None
• 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: MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
• 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

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

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

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

PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery

• Paper: https://arxiv.org/abs/2105.12990
• Code: None

Domain-Specific Suppression for Adaptive Object Detection

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

IQDet: Instance-wise Quality Distribution Sampling for Object Detection

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

Multi-Scale Aligned Distillation for Low-Resolution Detection

• Paper: https://jiaya.me/papers/ms\_align\_distill\_cvpr21.pdf
• Code: https://github.com/Jia-Research-Lab/MSAD

Adaptive Class Suppression Loss for Long-Tail Object Detection

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

VarifocalNet: An IoU-aware Dense Object Detector

• Paper(Oral): https://arxiv.org/abs/2008.13367
• Code: https://github.com/hyz-xmaster/VarifocalNet

Scale-aware Automatic Augmentation for Object Detection

• Paper: https://arxiv.org/abs/2103.17220
• Code: https://github.com/Jia-Research-Lab/SA-AutoAug

OTA: Optimal Transport Assignment for Object Detection

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

Distilling Object Detectors via Decoupled Features

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

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

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

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

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

Positive-Unlabeled Data Purification in the Wild for Object Detection

• Paper: None
• Code: None

Instance Localization for Self-supervised Detection Pretraining

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

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

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

End-to-End Object Detection with Fully Convolutional Network

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

Robust and Accurate Object Detection via Adversarial Learning

• Paper: https://arxiv.org/abs/2103.13886
• Code: None

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

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

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

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

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

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

YOLOF：You Only Look One-level Feature

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

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

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

General Instance Distillation for Object Detection

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

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

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

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

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

Multiple Instance Active Learning for Object Detection

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

Towards Open World Object Detection

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

Few-Shot目标检测

Adaptive Image Transformer for One-Shot Object Detection

• Paper: None
• Code: None

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

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

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

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

Few-Shot Object Detection via Contrastive Proposal Encoding

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

旋转目标检测

ReDet: A Rotation-equivariant Detector for Aerial Object Detection

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

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

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

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

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

Rethinking BiSeNet For Real-time Semantic Segmentation

• Paper: https://arxiv.org/abs/2104.13188
• Code: https://github.com/MichaelFan01/STDC-Seg

Progressive Semantic Segmentation

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

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

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

Bidirectional Projection Network for Cross Dimension Scene Understanding

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

Cross-Dataset Collaborative Learning for Semantic Segmentation

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

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

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

Capturing Omni-Range Context for Omnidirectional Segmentation

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

Learning Statistical Texture for Semantic Segmentation

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

PLOP: Learning without Forgetting for Continual Semantic Segmentation

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

弱监督语义分割

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

• Homepage: https://cvlab.yonsei.ac.kr/projects/BANA/
• Paper: https://arxiv.org/abs/2104.00905
• Code: None

Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

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

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

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

半监督语义分割

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

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

域自适应语义分割

Self-supervised Augmentation Consistency for Adapting Semantic Segmentation

• Paper: https://arxiv.org/abs/2105.00097
• Code: https://github.com/visinf/da-sac

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

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

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

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

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

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

Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation

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

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

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

视频语义分割

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

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

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: MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
• 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

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

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

3D医学图像分割

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

• Paper(Oral): https://arxiv.org/abs/2103.15954

• Code: None

Fourier Contour Embedding for Arbitrary-Shaped Text Detection

• Paper: https://arxiv.org/abs/2104.10442

• Code: None

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

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

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

Multi-Stage Progressive Image Restoration

• Paper: https://arxiv.org/abs/2102.02808

• Code: https://github.com/swz30/MPRNet

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

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

Towards Accurate Text-based Image Captioning with Content Diversity Exploration

• Paper: https://arxiv.org/abs/2105.03236

• Code: None

LoFTR: Detector-Free Local Feature Matching with Transformers

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

Convolutional Hough Matching Networks

• Homapage: http://cvlab.postech.ac.kr/research/CHM/

• Paper(Oral): https://arxiv.org/abs/2103.16831

• Code: None

Bridging the Visual Gap: Wide-Range Image Blending

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

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

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

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