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news 2025/7/4 18:35:24
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正文

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

語義分割(Semantic Segmentation)

======================================================================================

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

  • 作者單位: Facebook AI, 巴伊蘭大學(xué), 特拉維夫大學(xué)

  • 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

  • 作者單位: 美團(tuán)

  • Paper: https://arxiv.org/abs/2104.13188

  • Code: https://github.com/MichaelFan01/STDC-Seg

3. Progressive Semantic Segmentation

  • 作者單位: VinAI Research, VinUniversity, 阿肯色大學(xué), 石溪大學(xué)

  • 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

  • 作者單位: 復(fù)旦大學(xué), 牛津大學(xué), 薩里大學(xué), 騰訊優(yōu)圖, 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

  • 作者單位: 卡爾斯魯厄理工學(xué)院, 卡爾·蔡司, 華為

  • Paper: https://arxiv.org/abs/2103.05687

  • Code: None

6. Learning Statistical Texture for Semantic Segmentation

  • 作者單位: 北航, 商湯科技

  • Paper: https://arxiv.org/abs/2103.04133

  • Code: None

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

  • 作者單位: 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

  • 作者單位: 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

弱監(jiān)督語義分割

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

  • 作者單位: 延世大學(xué), 成均館大學(xué)

  • 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

  • 作者單位: 延世大學(xué)

  • 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

  • 作者單位: 南京理工大學(xué), MBZUAI, 電子科技大學(xué), 阿德萊德大學(xué), 悉尼科技大學(xué)

  • 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

  • 作者單位: 北京理工大學(xué), 美團(tuán)

  • 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

  • 作者單位: 首爾大學(xué)

  • Paper: https://arxiv.org/abs/2103.08907

  • Code: https://github.com/jbeomlee93/BBAM

半監(jiān)督語義分割

14. Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

  • 作者單位: 北京大學(xué), 微軟亞洲研究院

  • 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

  • 作者單位: 華為, 大連理工大學(xué), 北京大學(xué)

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

  • Code: None

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

  • 作者單位: 香港中文大學(xué), 思謀科技, 牛津大學(xué)

  • 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

  • 作者單位: NVIDIA, 多倫多大學(xué), 耶魯大學(xué), 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

  • 作者單位: ETH Zurich, 伯恩大學(xué), 魯汶大學(xué)

  • 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

域自適應(yīng)語義分割

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

  • 作者單位: ETH Zurich, 魯汶大學(xué), 電子科技大學(xué)

  • 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

  • 作者單位: 華東師范大學(xué)

  • 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

  • 作者單位: Idiap Research Institute, EPFL, 日內(nèi)瓦大學(xué)

  • 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

  • 作者單位: 達(dá)姆施塔特工業(yè)大學(xué), 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

  • 作者單位: 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

  • 作者單位: 香港大學(xué), 深睿醫(yī)療

  • Paper: https://arxiv.org/abs/2103.13041

  • Code: None

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

  • 作者單位: 香港城市大學(xué), 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

  • 作者單位: 華為云, 華為諾亞, 大連理工大學(xué)

  • Paper: https://arxiv.org/abs/2103.04717

  • Code: None

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

  • 作者單位: 中國科學(xué)技術(shù)大學(xué), 微軟亞洲研究院

  • 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

  • 作者單位: 南卡羅來納大學(xué), 天遠(yuǎn)視科技

  • 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語義分割

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

  • 作者單位: 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

  • 作者單位: 國科大, 清華大學(xué)

  • 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

無監(jiān)督語義分割

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

  • 作者單位: UT-Austin, 康奈爾大學(xué)

  • 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

視頻語義分割

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

  • 作者單位: 浙江大學(xué), 百度, 悉尼科技大學(xué)

  • 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

  • 作者單位: 帕多瓦大學(xué)

  • 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

  • 作者單位: 伊利諾伊大學(xué)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

  • 作者單位: Institute for Infocomm Research, 新加坡國立大學(xué)

  • 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

  • 作者單位: 索邦大學(xué), 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

  • 作者單位: 香港中文大學(xué), 香港大學(xué)

  • 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

  • 作者單位: 香港中文大學(xué), 牛津大學(xué)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

  • 作者單位: 北京大學(xué), 中科院, 國科大, ETH Zurich, 商湯科技等

  • 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

實(shí)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

視頻實(shí)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

醫(yī)學(xué)圖像分割

=================================================================

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

  • 作者單位: 騰訊天衍實(shí)驗室, 北京同仁醫(yī)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

  • 作者單位: 卡爾斯魯厄理工學(xué)院, 卡爾·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

  • 作者單位: 香港中文大學(xué), 香港理工大學(xué)

  • 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

  • 作者單位: 約翰斯·霍普金斯大大學(xué), 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

  • 作者單位: 斯坦福大學(xué)

  • 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

視頻目標(biāo)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

交互式視頻目標(biāo)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

顯著性檢測(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

偽裝物體檢測(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

協(xié)同顯著性檢測(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

協(xié)同顯著性檢測(Image Matting)

=================================================================================

Semantic Image Matting

  • Paper: https://arxiv.org/abs/2104.08201

  • Code: https://github.com/nowsyn/SIM

  • Dataset: https://github.com/nowsyn/SIM

行人重識別(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: 首個無需錨框（Anchor-Free）的行人搜索框架 | CVPR 2021

視頻理解/行為識別(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：最強(qiáng)時序動作提名修正網(wǎng)絡(luò)

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

人臉識別(Face Recognition)

=================================================================================

A 3D GAN for Improved Large-pose Facial Recognition

  • Paper: https://arxiv.org/abs/2012.10545

  • Code: None

MagFace: A Universal Representation for Face Recognition and Quality Assessment

  • Paper(Oral): https://arxiv.org/abs/2103.06627

  • Code: https://github.com/IrvingMeng/MagFace

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

  • Homepage: https://www.face-benchmark.org/

  • Paper: https://arxiv.org/abs/2103.04098

  • Dataset: https://www.face-benchmark.org/

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

  • Paper(Oral): https://arxiv.org/abs/2103.01520

  • Code: https://github.com/Hzzone/MTLFace

  • Dataset: https://github.com/Hzzone/MTLFace

人臉檢測(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

人臉活體檢測(Face Anti-Spoofing)

=====================================================================================

Cross Modal Focal Loss for RGBD Face Anti-Spoofing

  • Paper: https://arxiv.org/abs/2103.00948

  • Code: None

Deepfake檢測(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

人臉年齡估計(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

人臉表情識別(Facial Expression Recognition)

================================================================================================

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

  • Paper: https://arxiv.org/abs/2103.13372

  • Code: None

Deepfakes

====================================================================

MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes

  • Paper: https://arxiv.org/abs/2103.14211

  • Code: None

人體解析(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人體姿態(tài)估計(2D/3D Human Pose Estimation)

===================================================================================================

2D 人體姿態(tài)估計

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 人體姿態(tài)估計

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

動物姿態(tài)估計(Animal Pose Estimation)

=========================================================================================

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

  • Paper: https://arxiv.org/abs/2103.14843

  • Code: None

手部姿態(tài)估計(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

場景文本檢測(Scene Text Detection)

=======================================================================================

Fourier Contour Embedding for Arbitrary-Shaped Text Detection

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

  • Code: None

場景文本識別(Scene Text Recognition)

=========================================================================================

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

  • Paper: https://arxiv.org/abs/2103.06495

  • Code: https://github.com/FangShancheng/ABINet

圖像壓縮

===============================================================

Checkerboard Context Model for Efficient Learned Image Compression

  • Paper: https://arxiv.org/abs/2103.15306

  • Code: None

Slimmable Compressive Autoencoders for Practical Neural Image Compression

  • Paper: https://arxiv.org/abs/2103.15726

  • Code: None

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

  • Paper: https://arxiv.org/abs/2103.15368

  • Code: None

模型壓縮/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

知識蒸餾(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

去霧(Dehazing)

=======================================================================

Contrastive Learning for Compact Single Image Dehazing

  • Paper: https://arxiv.org/abs/2104.09367

  • Code: https://github.com/GlassyWu/AECR-Net

視頻超分辨率

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

  • Paper: None

  • Code: https://github.com/CS-GangXu/TMNet

圖像恢復(fù)(Image Restoration)

==================================================================================

Multi-Stage Progressive Image Restoration

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

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

圖像補(bǔ)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

圖像編輯(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

圖像描述(Image Captioning)

=================================================================================

Towards Accurate Text-based Image Captioning with Content Diversity Exploration

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

  • Code: None

字體生成(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

圖像匹配(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

圖像融合(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點(diǎn)云分類(3D Point Clouds Classification)

=================================================================================================

Equivariant Point Network for 3D Point Cloud Analysis

  • Paper: https://arxiv.org/abs/2103.14147

  • Code: None

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

  • Paper: https://arxiv.org/abs/2103.14635

  • Code: https://github.com/CVMI-Lab/PAConv

3D目標(biāo)檢測(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語義分割(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目標(biāo)跟蹤(3D Object Trancking)

======================================================================================

Center-based 3D Object Detection and Tracking

  • Paper: https://arxiv.org/abs/2006.11275

  • Code: https://github.com/tianweiy/CenterPoint

3D點(diǎn)云配準(zhǔn)(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點(diǎn)云補(bǔ)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位姿估計(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

相機(jī)姿態(tài)估計

=================================================================

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

深度估計(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

立體匹配(Stereo Matching)

================================================================================

A Decomposition Model for Stereo Matching

  • Paper: https://arxiv.org/abs/2104.07516

  • Code: None

光流估計(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

車道線檢測(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

軌跡預(yù)測(Trajectory Prediction)

======================================================================================

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

  • Paper(Oral): https://arxiv.org/abs/2104.08277

  • Code: None

人群計數(shù)(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

對抗樣本(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

圖像檢索(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

視頻檢索(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

跨模態(tài)檢索(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

聯(lián)邦學(xué)習(xí)(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

視頻插幀(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

視覺推理(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

圖像合成(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

視圖合成(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

風(fēng)格遷移(Style Transfer)

===============================================================================

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

  • Paper: https://arxiv.org/abs/2104.05376

  • Code: https://github.com/PaddlePaddle/PaddleGAN/

布局生成(Layout Generation)

==================================================================================

LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity

  • Paper: None

  • Code: None

Variational Transformer Networks for Layout Generation

  • Paper: https://arxiv.org/abs/2104.02416

  • Code: None

Domain Generalization

================================================================================

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

  • Paper: https://arxiv.org/abs/2105.09156

  • Code: None

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

  • Paper: https://arxiv.org/abs/2103.15597

  • Code: https://github.com/shachoi/RobustNet

Adaptive Methods for Real-World Domain Generalization

  • Paper: https://arxiv.org/abs/2103.15796

  • Code: None

FSDR: Frequency Space Domain Randomization for Domain Generalization

  • Paper: https://arxiv.org/abs/2103.02370

  • Code: None

Domain Adaptation

============================================================================

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

  • Paper: https://arxiv.org/abs/2104.00808

  • Code: None

Domain Consensus Clustering for Universal Domain Adaptation

  • Paper: http://reler.net/papers/guangrui_cvpr2021.pdf

  • Code: https://github.com/Solacex/Domain-Consensus-Clustering

Open-Set

===================================================================

Towards Open World Object Detection

  • Paper(Oral): https://arxiv.org/abs/2103.02603

  • Code: https://github.com/JosephKJ/OWOD

Exemplar-Based Open-Set Panoptic Segmentation Network

  • Homepage: https://cv.snu.ac.kr/research/EOPSN/

  • Paper: https://arxiv.org/abs/2105.08336

  • Code: https://github.com/jd730/EOPSN

Learning Placeholders for Open-Set Recognition

  • Paper(Oral): https://arxiv.org/abs/2103.15086

  • Code: None

Adversarial Attack

=============================================================================

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

  • Paper: https://arxiv.org/abs/2103.14938

  • Code: https://github.com/VISION-SJTU/IoUattack

"人-物"交互(HOI)檢測

=========================================================================

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

陰影去除(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

虛擬換衣(Virtual Try-On)

===============================================================================

Parser-Free Virtual Try-on via Distilling Appearance Flows

基于外觀流蒸餾的無需人體解析的虛擬換裝

  • Paper: https://arxiv.org/abs/2103.04559

  • Code: https://github.com/geyuying/PF-AFN

標(biāo)簽噪聲(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

視頻穩(wěn)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

數(shù)據(jù)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

論文下載鏈接：

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！騰訊優(yōu)圖20篇論文入選CVPR 2021

  • MePro團(tuán)隊三篇論文被CVPR 2021接收

不確定中沒中(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

網(wǎng)上學(xué)習(xí)資料一大堆，但如果學(xué)到的知識不成體系，遇到問題時只是淺嘗輒止，不再深入研究，那么很難做到真正的技術(shù)提升。

需要這份系統(tǒng)化的資料的朋友，可以添加V獲取：vip1024c （備注python）
img

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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！騰訊優(yōu)圖20篇論文入選CVPR 2021

  • MePro團(tuán)隊三篇論文被CVPR 2021接收

不確定中沒中(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

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