CVPR2020-Code

CVPR 2020 论文开源项目合集，同时欢迎各位大佬提交issue，分享CVPR 2020开源项目
【推荐阅读】

• CVPR 2020 virtual
• ECCV 2020 论文开源项目合集来了：https://github.com/amusi/ECCV2020-Code
• 关于往年CV顶会论文（如ECCV 2020、CVPR 2019、ICCV 2019）以及其他优质CV论文和大盘点，详见： https://github.com/amusi/daily-paper-computer-vision

【CVPR 2020 论文开源目录】

• CNN
• 图像分类
• 视频分类
• 目标检测
• 3D目标检测
• 视频目标检测
• 目标跟踪
• 语义分割
• 实例分割
• 全景分割
• 视频目标分割
• 超像素分割
• 交互式图像分割
• NAS
• GAN
• Re-ID
• 3D点云（分类/分割/配准/跟踪等）
• 人脸（识别/检测/重建等）
• 人体姿态估计(2D/3D)
• 人体解析
• 场景文本检测
• 场景文本识别
• 特征(点)检测和描述
• 超分辨率
• 模型压缩/剪枝
• 视频理解/行为识别
• 人群计数
• 深度估计
• 6D目标姿态估计
• 手势估计
• 显著性检测
• 去噪
• 去雨
• 去模糊
• 去雾
• 特征点检测与描述
• 视觉问答(VQA)
• 视频问答(VideoQA)
• 视觉语言导航
• 视频压缩
• 视频插帧
• 风格迁移
• 车道线检测
• “人-物”交互(HOI)检测
• 轨迹预测
• 运动预测
• 光流估计
• 图像检索
• 虚拟试衣
• HDR
• 对抗样本
• 三维重建
• 深度补全
• 语义场景补全
• 图像/视频描述
• 线框解析
• 数据集
• 其他

• 不确定中没中

  CNN

  Exploring Self-attention for Image Recognition

• 论文：https://hszhao.github.io/papers/cvpr20_san.pdf

• 代码：https://github.com/hszhao/SAN

Improving Convolutional Networks with Self-Calibrated Convolutions

• 主页：https://mmcheng.net/scconv/
• 论文：http://mftp.mmcheng.net/Papers/20cvprSCNet.pdf
• 代码：https://github.com/backseason/SCNet

Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets

• 论文：https://arxiv.org/abs/2003.13549

• 代码：https://github.com/zeiss-microscopy/BSConv

  图像分类

  Interpretable and Accurate Fine-grained Recognition via Region Grouping

• 论文：https://arxiv.org/abs/2005.10411

• 代码：https://github.com/zxhuang1698/interpretability-by-parts

Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion

• 论文：https://arxiv.org/abs/2003.04490
• 代码：https://github.com/AdamKortylewski/CompositionalNets

Spatially Attentive Output Layer for Image Classification

• 论文：https://arxiv.org/abs/2004.07570

• 代码（好像被原作者删除了）：https://github.com/ildoonet/spatially-attentive-output-layer

  视频分类

  SmallBigNet: Integrating Core and Contextual Views for Video Classification

• 论文：https://arxiv.org/abs/2006.14582

• 代码：https://github.com/xhl-video/SmallBigNet

  目标检测

  Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf

• 代码：https://github.com/FishYuLi/BalancedGroupSoftmax

AugFPN: Improving Multi-scale Feature Learning for Object Detection

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_AugFPN_Improving_Multi-Scale_Feature_Learning_for_Object_Detection_CVPR_2020_paper.pdf
• 代码：https://github.com/Gus-Guo/AugFPN

Noise-Aware Fully Webly Supervised Object Detection

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Noise-Aware_Fully_Webly_Supervised_Object_Detection_CVPR_2020_paper.html
• 代码：https://github.com/shenyunhang/NA-fWebSOD/

Learning a Unified Sample Weighting Network for Object Detection

• 论文：https://arxiv.org/abs/2006.06568
• 代码：https://github.com/caiqi/sample-weighting-network

D2Det: Towards High Quality Object Detection and Instance Segmentation

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf
• 代码：https://github.com/JialeCao001/D2Det

Dynamic Refinement Network for Oriented and Densely Packed Object Detection

• 论文下载链接：https://arxiv.org/abs/2005.09973
• 代码和数据集：https://github.com/Anymake/DRN_CVPR2020

Scale-Equalizing Pyramid Convolution for Object Detection
论文：https://arxiv.org/abs/2005.03101
代码：https://github.com/jshilong/SEPC
Revisiting the Sibling Head in Object Detector

• 论文：https://arxiv.org/abs/2003.07540
• 代码：https://github.com/Sense-X/TSD

Scale-equalizing Pyramid Convolution for Object Detection

• 论文：暂无
• 代码：https://github.com/jshilong/SEPC

Detection in Crowded Scenes: One Proposal, Multiple Predictions

• 论文：https://arxiv.org/abs/2003.09163
• 代码：https://github.com/megvii-model/CrowdDetection

Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection

• 论文：https://arxiv.org/abs/2004.04725
• 代码：https://github.com/NVlabs/wetectron

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

• 论文：https://arxiv.org/abs/1912.02424
• 代码：https://github.com/sfzhang15/ATSS

BiDet: An Efficient Binarized Object Detector

• 论文：https://arxiv.org/abs/2003.03961
• 代码：https://github.com/ZiweiWangTHU/BiDet

Harmonizing Transferability and Discriminability for Adapting Object Detectors

• 论文：https://arxiv.org/abs/2003.06297
• 代码：https://github.com/chaoqichen/HTCN

CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection

• 论文：https://arxiv.org/abs/2003.09119
• 代码：https://github.com/KiveeDong/CentripetalNet

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection

• 论文：https://arxiv.org/abs/2003.11818
• 代码：https://github.com/ggjy/HitDet.pytorch

EfficientDet: Scalable and Efficient Object Detection

• 论文：https://arxiv.org/abs/1911.09070

• 代码：https://github.com/google/automl/tree/master/efficientdet

  3D目标检测

  SESS: Self-Ensembling Semi-Supervised 3D Object Detection

• 论文： https://arxiv.org/abs/1912.11803

• 代码：https://github.com/Na-Z/sess

Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection

• 论文： https://arxiv.org/abs/2006.04356
• 代码：https://github.com/dleam/Associate-3Ddet

What You See is What You Get: Exploiting Visibility for 3D Object Detection

• 主页：https://www.cs.cmu.edu/~peiyunh/wysiwyg/
• 论文：https://arxiv.org/abs/1912.04986
• 代码：https://github.com/peiyunh/wysiwyg

Learning Depth-Guided Convolutions for Monocular 3D Object Detection

• 论文：https://arxiv.org/abs/1912.04799
• 代码：https://github.com/dingmyu/D4LCN

Structure Aware Single-stage 3D Object Detection from Point Cloud

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/He_Structure_Aware_Single-Stage_3D_Object_Detection_From_Point_Cloud_CVPR_2020_paper.html
• 代码：https://github.com/skyhehe123/SA-SSD

IDA-3D: Instance-Depth-Aware 3D Object Detection from Stereo Vision for Autonomous Driving

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Peng_IDA-3D_Instance-Depth-Aware_3D_Object_Detection_From_Stereo_Vision_for_Autonomous_CVPR_2020_paper.pdf
• 代码：https://github.com/swords123/IDA-3D

Train in Germany, Test in The USA: Making 3D Object Detectors Generalize

• 论文：https://arxiv.org/abs/2005.08139
• 代码：https://github.com/cxy1997/3D_adapt_auto_driving

MLCVNet: Multi-Level Context VoteNet for 3D Object Detection

• 论文：https://arxiv.org/abs/2004.05679
• 代码：https://github.com/NUAAXQ/MLCVNet

3DSSD: Point-based 3D Single Stage Object Detector

• CVPR 2020 Oral
• 论文：https://arxiv.org/abs/2002.10187
• 代码：https://github.com/tomztyang/3DSSD

Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

• 论文：https://arxiv.org/abs/2004.03572
• 代码：https://github.com/zju3dv/disprcn

End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

• 论文：https://arxiv.org/abs/2004.03080
• 代码：https://github.com/mileyan/pseudo-LiDAR_e2e

DSGN: Deep Stereo Geometry Network for 3D Object Detection

• 论文：https://arxiv.org/abs/2001.03398
• 代码：https://github.com/chenyilun95/DSGN

LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention

• 论文：https://arxiv.org/abs/2004.01389
• 代码：https://github.com/yinjunbo/3DVID

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

• 论文：https://arxiv.org/abs/1912.13192
• 代码：https://github.com/sshaoshuai/PV-RCNN

Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

• 论文：https://arxiv.org/abs/2003.01251

• 代码：https://github.com/WeijingShi/Point-GNN

  视频目标检测

  Memory Enhanced Global-Local Aggregation for Video Object Detection
  论文：https://arxiv.org/abs/2003.12063
  代码：https://github.com/Scalsol/mega.pytorch

  目标跟踪

  SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking

• 论文：https://arxiv.org/abs/1911.07241

• 代码：https://github.com/ohhhyeahhh/SiamCAR

D3S — A Discriminative Single Shot Segmentation Tracker

• 论文：https://arxiv.org/abs/1911.08862
• 代码：https://github.com/alanlukezic/d3s

ROAM: Recurrently Optimizing Tracking Model

• 论文：https://arxiv.org/abs/1907.12006
• 代码：https://github.com/skyoung/ROAM

Siam R-CNN: Visual Tracking by Re-Detection

• 主页：https://www.vision.rwth-aachen.de/page/siamrcnn
• 论文：https://arxiv.org/abs/1911.12836
• 论文2：https://www.vision.rwth-aachen.de/media/papers/192/siamrcnn.pdf
• 代码：https://github.com/VisualComputingInstitute/SiamR-CNN

Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises

• 论文：https://arxiv.org/abs/2003.09595
• 代码：https://github.com/MasterBin-IIAU/CSA

High-Performance Long-Term Tracking with Meta-Updater

• 论文：https://arxiv.org/abs/2004.00305
• 代码：https://github.com/Daikenan/LTMU

AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization

• 论文：https://arxiv.org/abs/2003.12949
• 代码：https://github.com/vision4robotics/AutoTrack

Probabilistic Regression for Visual Tracking

• 论文：https://arxiv.org/abs/2003.12565
• 代码：https://github.com/visionml/pytracking

MAST: A Memory-Augmented Self-supervised Tracker

• 论文：https://arxiv.org/abs/2002.07793
• 代码：https://github.com/zlai0/MAST

Siamese Box Adaptive Network for Visual Tracking

• 论文：https://arxiv.org/abs/2003.06761

• 代码：https://github.com/hqucv/siamban

  多目标跟踪

  3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset

• 主页：https://vap.aau.dk/3d-zef/

• 论文：https://arxiv.org/abs/2006.08466
• 代码：https://bitbucket.org/aauvap/3d-zef/src/master/

• 数据集：https://motchallenge.net/data/3D-ZeF20

  语义分割

  FDA: Fourier Domain Adaptation for Semantic Segmentation

• 论文：https://arxiv.org/abs/2004.05498

• 代码：https://github.com/YanchaoYang/FDA

Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation

• 论文：暂无
• 代码：https://github.com/JianqiangWan/Super-BPD

Single-Stage Semantic Segmentation from Image Labels

• 论文：https://arxiv.org/abs/2005.08104
• 代码：https://github.com/visinf/1-stage-wseg

Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation

• 论文：https://arxiv.org/abs/2003.00867
• 代码：https://github.com/MyeongJin-Kim/Learning-Texture-Invariant-Representation

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

• 论文：http://vladlen.info/papers/MSeg.pdf
• 代码：https://github.com/mseg-dataset/mseg-api

CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

• 论文：https://arxiv.org/abs/2005.02551
• 代码：https://github.com/hkchengrex/CascadePSP

Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision

• Oral
• 论文：https://arxiv.org/abs/2004.07703
• 代码：https://github.com/feipan664/IntraDA

Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

• 论文：https://arxiv.org/abs/2004.04581
• 代码：https://github.com/YudeWang/SEAM

Temporally Distributed Networks for Fast Video Segmentation

• 论文：https://arxiv.org/abs/2004.01800
• 代码：https://github.com/feinanshan/TDNet

Context Prior for Scene Segmentation

• 论文：https://arxiv.org/abs/2004.01547
• 代码：https://git.io/ContextPrior

Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

• 论文：https://arxiv.org/abs/2003.13328
• 代码：https://github.com/Andrew-Qibin/SPNet

Cars Can’t Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks

• 论文：https://arxiv.org/abs/2003.05128
• 代码：https://github.com/shachoi/HANet

Learning Dynamic Routing for Semantic Segmentation

• 论文：https://arxiv.org/abs/2003.10401

• 代码：https://github.com/yanwei-li/DynamicRouting

  实例分割

  D2Det: Towards High Quality Object Detection and Instance Segmentation

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf

• 代码：https://github.com/JialeCao001/D2Det

PolarMask: Single Shot Instance Segmentation with Polar Representation

• 论文：https://arxiv.org/abs/1909.13226
• 代码：https://github.com/xieenze/PolarMask
• 解读：https://zhuanlan.zhihu.com/p/84890413

CenterMask : Real-Time Anchor-Free Instance Segmentation

• 论文：https://arxiv.org/abs/1911.06667
• 代码：https://github.com/youngwanLEE/CenterMask

BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

• 论文：https://arxiv.org/abs/2001.00309
• 代码：https://github.com/aim-uofa/AdelaiDet

Deep Snake for Real-Time Instance Segmentation

• 论文：https://arxiv.org/abs/2001.01629
• 代码：https://github.com/zju3dv/snake

Mask Encoding for Single Shot Instance Segmentation

• 论文：https://arxiv.org/abs/2003.11712

• 代码：https://github.com/aim-uofa/AdelaiDet

  全景分割

  Video Panoptic Segmentation

• 论文：https://arxiv.org/abs/2006.11339

• 代码：https://github.com/mcahny/vps
• 数据集：https://www.dropbox.com/s/ecem4kq0fdkver4/cityscapes-vps-dataset-1.0.zip?dl=0

Pixel Consensus Voting for Panoptic Segmentation

• 论文：https://arxiv.org/abs/2004.01849
• 代码：还未公布

BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation
论文：https://arxiv.org/abs/2003.14031
代码：https://github.com/Mooonside/BANet

视频目标分割

A Transductive Approach for Video Object Segmentation

• 论文：https://arxiv.org/abs/2004.07193
• 代码：https://github.com/microsoft/transductive-vos.pytorch

State-Aware Tracker for Real-Time Video Object Segmentation

• 论文：https://arxiv.org/abs/2003.00482
• 代码：https://github.com/MegviiDetection/video_analyst

Learning Fast and Robust Target Models for Video Object Segmentation

• 论文：https://arxiv.org/abs/2003.00908
• 代码：https://github.com/andr345/frtm-vos

Learning Video Object Segmentation from Unlabeled Videos

• 论文：https://arxiv.org/abs/2003.05020

• 代码：https://github.com/carrierlxk/MuG

  超像素分割

  Superpixel Segmentation with Fully Convolutional Networks

• 论文：https://arxiv.org/abs/2003.12929

• 代码：https://github.com/fuy34/superpixel_fcn

  交互式图像分割

  Interactive Object Segmentation with Inside-Outside Guidance

• 论文下载链接：http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Interactive_Object_Segmentation_With_Inside-Outside_Guidance_CVPR_2020_paper.pdf

• 代码：https://github.com/shiyinzhang/Inside-Outside-Guidance

• 数据集：https://github.com/shiyinzhang/Pixel-ImageNet

  NAS

  AOWS: Adaptive and optimal network width search with latency constraints

• 论文：https://arxiv.org/abs/2005.10481

• 代码：https://github.com/bermanmaxim/AOWS

Densely Connected Search Space for More Flexible Neural Architecture Search

• 论文：https://arxiv.org/abs/1906.09607
• 代码：https://github.com/JaminFong/DenseNAS

MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning

• 论文：https://arxiv.org/abs/2003.14058
• 代码：https://github.com/bhpfelix/MTLNAS

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

• 论文下载链接：https://arxiv.org/abs/2004.05565
• 代码：https://github.com/facebookresearch/mobile-vision

Neural Architecture Search for Lightweight Non-Local Networks

• 论文：https://arxiv.org/abs/2004.01961
• 代码：https://github.com/LiYingwei/AutoNL

Rethinking Performance Estimation in Neural Architecture Search

• 论文：https://arxiv.org/abs/2005.09917
• 代码：https://github.com/zhengxiawu/rethinking_performance_estimation_in_NAS
• 解读1：https://www.zhihu.com/question/372070853/answer/1035234510
• 解读2：https://zhuanlan.zhihu.com/p/111167409

CARS: Continuous Evolution for Efficient Neural Architecture Search

• 论文：https://arxiv.org/abs/1909.04977

• 代码（即将开源）：https://github.com/huawei-noah/CARS

  GAN

  SEAN: Image Synthesis with Semantic Region-Adaptive Normalization

• 论文：https://arxiv.org/abs/1911.12861

• 代码：https://github.com/ZPdesu/SEAN

Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

• 论文地址：http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Reusing_Discriminators_for_Encoding_Towards_Unsupervised_Image-to-Image_Translation_CVPR_2020_paper.html
• 代码地址：https://github.com/alpc91/NICE-GAN-pytorch

Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning

• 论文：https://arxiv.org/abs/1912.01899
• 代码：https://github.com/SsGood/DBGAN

PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer

• 论文：https://arxiv.org/abs/1909.06956
• 代码：https://github.com/wtjiang98/PSGAN

Semantically Mutil-modal Image Synthesis

• 主页：http://seanseattle.github.io/SMIS
• 论文：https://arxiv.org/abs/2003.12697
• 代码：https://github.com/Seanseattle/SMIS

Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping

• 论文：https://yiranran.github.io/files/CVPR2020_Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping.pdf
• 代码：https://github.com/yiranran/Unpaired-Portrait-Drawing

Learning to Cartoonize Using White-box Cartoon Representations

• 论文：https://github.com/SystemErrorWang/White-box-Cartoonization/blob/master/paper/06791.pdf
• 主页：https://systemerrorwang.github.io/White-box-Cartoonization/
• 代码：https://github.com/SystemErrorWang/White-box-Cartoonization
• 解读：https://zhuanlan.zhihu.com/p/117422157
• Demo视频：https://www.bilibili.com/video/av56708333

GAN Compression: Efficient Architectures for Interactive Conditional GANs

• 论文：https://arxiv.org/abs/2003.08936
• 代码：https://github.com/mit-han-lab/gan-compression

Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions

• 论文：https://arxiv.org/abs/2003.01826

• 代码：https://github.com/cc-hpc-itwm/UpConv

  Re-ID

  High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_High-Order_Information_Matters_Learning_Relation_and_Topology_for_Occluded_Person_CVPR_2020_paper.html

• 代码：https://github.com/wangguanan/HOReID

COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification

• 论文：https://arxiv.org/abs/2005.07862
• 数据集：暂无

Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking

• 论文：https://arxiv.org/abs/2004.04199
• 代码：https://github.com/whj363636/Adversarial-attack-on-Person-ReID-With-Deep-Mis-Ranking

Pose-guided Visible Part Matching for Occluded Person ReID

• 论文：https://arxiv.org/abs/2004.00230
• 代码：https://github.com/hh23333/PVPM

Weakly supervised discriminative feature learning with state information for person identification

• 论文：https://arxiv.org/abs/2002.11939

• 代码：https://github.com/KovenYu/state-information

  3D点云（分类/分割/配准等）

  3D点云卷积

  PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling

• 论文：https://arxiv.org/abs/2003.00492

• 代码：https://github.com/yanx27/PointASNL

Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds

• 论文下载链接：https://arxiv.org/abs/2003.12971
• 代码：https://github.com/raoyongming/PointGLR

Grid-GCN for Fast and Scalable Point Cloud Learning

• 论文：https://arxiv.org/abs/1912.02984
• 代码：https://github.com/Xharlie/Grid-GCN

FPConv: Learning Local Flattening for Point Convolution

• 论文：https://arxiv.org/abs/2002.10701

• 代码：https://github.com/lyqun/FPConv

  3D点云分类

  PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

• 论文：https://arxiv.org/abs/2002.10876

• 代码（即将开源）： https://github.com/liruihui/PointAugment/

  3D点云语义分割

  RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

• 论文：https://arxiv.org/abs/1911.11236

• 代码：https://github.com/QingyongHu/RandLA-Net
• 解读：https://zhuanlan.zhihu.com/p/105433460

Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels

• 论文：https://arxiv.org/abs/2004.04091
• 代码：https://github.com/alex-xun-xu/WeakSupPointCloudSeg

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

• 论文：https://arxiv.org/abs/2003.14032
• 代码：https://github.com/edwardzhou130/PolarSeg

Learning to Segment 3D Point Clouds in 2D Image Space

• 论文：https://arxiv.org/abs/2003.05593

• 代码：https://github.com/WPI-VISLab/Learning-to-Segment-3D-Point-Clouds-in-2D-Image-Space

  3D点云实例分割

  PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation

• 论文：https://arxiv.org/abs/2004.01658

• 代码：https://github.com/Jia-Research-Lab/PointGroup

  3D点云配准

  Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences

• 论文：https://arxiv.org/abs/2005.01014

• 代码：https://github.com/XiaoshuiHuang/fmr

D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features

• 论文：https://arxiv.org/abs/2003.03164
• 代码：https://github.com/XuyangBai/D3Feat

RPM-Net: Robust Point Matching using Learned Features

• 论文：https://arxiv.org/abs/2003.13479

• 代码：https://github.com/yewzijian/RPMNet

  3D点云补全

  Cascaded Refinement Network for Point Cloud Completion

• 论文：https://arxiv.org/abs/2004.03327

• 代码：https://github.com/xiaogangw/cascaded-point-completion

  3D点云目标跟踪

  P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds

• 论文：https://arxiv.org/abs/2005.13888

• 代码：https://github.com/HaozheQi/P2B

  其他

  An Efficient PointLSTM for Point Clouds Based Gesture Recognition

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Min_An_Efficient_PointLSTM_for_Point_Clouds_Based_Gesture_Recognition_CVPR_2020_paper.html

• 代码：https://github.com/Blueprintf/pointlstm-gesture-recognition-pytorch

  人脸

  人脸识别

  CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition

• 论文：https://arxiv.org/abs/2004.00288

• 代码：https://github.com/HuangYG123/CurricularFace

Learning Meta Face Recognition in Unseen Domains

• 论文：https://arxiv.org/abs/2003.07733
• 代码：https://github.com/cleardusk/MFR

• 解读：https://mp.weixin.qq.com/s/YZoEnjpnlvb90qSI3xdJqQ

  人脸检测

  人脸活体检测

  Searching Central Difference Convolutional Networks for Face Anti-Spoofing

• 论文：https://arxiv.org/abs/2003.04092

• 代码：https://github.com/ZitongYu/CDCN

  人脸表情识别

  Suppressing Uncertainties for Large-Scale Facial Expression Recognition

• 论文：https://arxiv.org/abs/2002.10392

• 代码（即将开源）：https://github.com/kaiwang960112/Self-Cure-Network

  人脸转正

  Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images

• 论文：https://arxiv.org/abs/2003.08124

• 代码：https://github.com/Hangz-nju-cuhk/Rotate-and-Render

  人脸3D重建

  AvatarMe: Realistically Renderable 3D Facial Reconstruction “in-the-wild”

• 论文：https://arxiv.org/abs/2003.13845

• 数据集：https://github.com/lattas/AvatarMe

FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

• 论文：https://arxiv.org/abs/2003.13989

• 代码：https://github.com/zhuhao-nju/facescape

  人体姿态估计(2D/3D)

  2D人体姿态估计

  TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting

• 主页：https://yzhq97.github.io/transmomo/

• 论文：https://arxiv.org/abs/2003.14401
• 代码：https://github.com/yzhq97/transmomo.pytorch

HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

• 论文：https://arxiv.org/abs/1908.10357
• 代码：https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation

The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation

• 论文：https://arxiv.org/abs/1911.07524
• 代码：https://github.com/HuangJunJie2017/UDP-Pose
• 解读：https://zhuanlan.zhihu.com/p/92525039

Distribution-Aware Coordinate Representation for Human Pose Estimation

• 主页：https://ilovepose.github.io/coco/
• 论文：https://arxiv.org/abs/1910.06278

• 代码：https://github.com/ilovepose/DarkPose

  3D人体姿态估计

  Cascaded Deep Monocular 3D Human Pose Estimation With Evolutionary Training Data

• 论文：https://arxiv.org/abs/2006.07778

• 代码：https://github.com/Nicholasli1995/EvoSkeleton

Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach

• 主页：https://www.zhe-zhang.com/cvpr2020
• 论文：https://arxiv.org/abs/2003.11163
• 代码：https://github.com/CHUNYUWANG/imu-human-pose-pytorch

Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data

• 论文下载链接：https://arxiv.org/abs/2004.01166
• 代码：https://github.com/Healthcare-Robotics/bodies-at-rest
• 数据集：https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KOA4ML

Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis

• 主页：http://val.cds.iisc.ac.in/pgp-human/
• 论文：https://arxiv.org/abs/2004.04400

Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation

• 论文：https://arxiv.org/abs/2004.00329
• 代码：https://github.com/fabbrimatteo/LoCO

VIBE: Video Inference for Human Body Pose and Shape Estimation

• 论文：https://arxiv.org/abs/1912.05656
• 代码：https://github.com/mkocabas/VIBE

Back to the Future: Joint Aware Temporal Deep Learning 3D Human Pose Estimation

• 论文：https://arxiv.org/abs/2002.11251
• 代码：https://github.com/vnmr/JointVideoPose3D

Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS

• 论文：https://arxiv.org/abs/2003.03972

• 数据集：暂无

  人体解析

  Correlating Edge, Pose with Parsing

• 论文：https://arxiv.org/abs/2005.01431

• 代码：https://github.com/ziwei-zh/CorrPM

  场景文本检测

  STEFANN: Scene Text Editor using Font Adaptive Neural Network

• 主页：https://prasunroy.github.io/stefann/

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Roy_STEFANN_Scene_Text_Editor_Using_Font_Adaptive_Neural_Network_CVPR_2020_paper.html
• 代码：https://github.com/prasunroy/stefann
• 数据集：https://drive.google.com/open?id=1sEDiX_jORh2X-HSzUnjIyZr-G9LJIw1k

ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_ContourNet_Taking_a_Further_Step_Toward_Accurate_Arbitrary-Shaped_Scene_Text_CVPR_2020_paper.pdf
• 代码：https://github.com/wangyuxin87/ContourNet

UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World

• 论文：https://arxiv.org/abs/2003.10608
• 代码和数据集：https://github.com/Jyouhou/UnrealText/

ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

• 论文：https://arxiv.org/abs/2002.10200
• 代码（即将开源）：https://github.com/Yuliang-Liu/bezier_curve_text_spotting
• 代码（即将开源）：https://github.com/aim-uofa/adet

Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection

• 论文：https://arxiv.org/abs/2003.07493

• 代码：https://github.com/GXYM/DRRG

  场景文本识别

  SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition

• 论文：https://arxiv.org/abs/2005.10977

• 代码：https://github.com/Pay20Y/SEED

UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World

• 论文：https://arxiv.org/abs/2003.10608
• 代码和数据集：https://github.com/Jyouhou/UnrealText/

ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

• 论文：https://arxiv.org/abs/2002.10200
• 代码（即将开源）：https://github.com/aim-uofa/adet

Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition

• 论文：https://arxiv.org/abs/2003.06606

• 代码：https://github.com/Canjie-Luo/Text-Image-Augmentation

  特征(点)检测和描述

  SuperGlue: Learning Feature Matching with Graph Neural Networks

• 论文：https://arxiv.org/abs/1911.11763

• 代码：https://github.com/magicleap/SuperGluePretrainedNetwork

  超分辨率

  图像超分辨率

  Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Closed-Loop_Matters_Dual_Regression_Networks_for_Single_Image_Super-Resolution_CVPR_2020_paper.html

• 代码：https://github.com/guoyongcs/DRN

Learning Texture Transformer Network for Image Super-Resolution

• 论文：https://arxiv.org/abs/2006.04139
• 代码：https://github.com/FuzhiYang/TTSR

Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

• 论文：https://arxiv.org/abs/2006.01424
• 代码：https://github.com/SHI-Labs/Cross-Scale-Non-Local-Attention

Structure-Preserving Super Resolution with Gradient Guidance

• 论文：https://arxiv.org/abs/2003.13081
• 代码：https://github.com/Maclory/SPSR

Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
论文：https://arxiv.org/abs/2004.00448
代码：https://github.com/clovaai/cutblur

视频超分辨率

TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution

• 论文：https://arxiv.org/abs/1812.02898
• 代码：https://github.com/YapengTian/TDAN-VSR-CVPR-2020

Space-Time-Aware Multi-Resolution Video Enhancement

• 主页：https://alterzero.github.io/projects/STAR.html
• 论文：http://arxiv.org/abs/2003.13170
• 代码：https://github.com/alterzero/STARnet

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

• 论文：https://arxiv.org/abs/2002.11616

• 代码：https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020

  模型压缩/剪枝

  DMCP: Differentiable Markov Channel Pruning for Neural Networks

• 论文：https://arxiv.org/abs/2005.03354

• 代码：https://github.com/zx55/dmcp

Forward and Backward Information Retention for Accurate Binary Neural Networks

• 论文：https://arxiv.org/abs/1909.10788
• 代码：https://github.com/htqin/IR-Net

Towards Efficient Model Compression via Learned Global Ranking

• 论文：https://arxiv.org/abs/1904.12368
• 代码：https://github.com/cmu-enyac/LeGR

HRank: Filter Pruning using High-Rank Feature Map

• 论文：http://arxiv.org/abs/2002.10179
• 代码：https://github.com/lmbxmu/HRank

GAN Compression: Efficient Architectures for Interactive Conditional GANs

• 论文：https://arxiv.org/abs/2003.08936
• 代码：https://github.com/mit-han-lab/gan-compression

Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression

• 论文：https://arxiv.org/abs/2003.08935

• 代码：https://github.com/ofsoundof/group_sparsity

  视频理解/行为识别

  Oops! Predicting Unintentional Action in Video

• 主页：https://oops.cs.columbia.edu/

• 论文：https://arxiv.org/abs/1911.11206
• 代码：https://github.com/cvlab-columbia/oops
• 数据集：https://oops.cs.columbia.edu/data

PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition

• 论文：https://arxiv.org/abs/1911.12409
• 代码：https://github.com/shlizee/Predict-Cluster

Intra- and Inter-Action Understanding via Temporal Action Parsing

• 论文：https://arxiv.org/abs/2005.10229
• 主页和数据集：https://sdolivia.github.io/TAPOS/

3DV: 3D Dynamic Voxel for Action Recognition in Depth Video

• 论文：https://arxiv.org/abs/2005.05501
• 代码：https://github.com/3huo/3DV-Action

FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding

• 主页：https://sdolivia.github.io/FineGym/
• 论文：https://arxiv.org/abs/2004.06704

TEA: Temporal Excitation and Aggregation for Action Recognition

• 论文：https://arxiv.org/abs/2004.01398
• 代码：https://github.com/Phoenix1327/tea-action-recognition

X3D: Expanding Architectures for Efficient Video Recognition

• 论文：https://arxiv.org/abs/2004.04730
• 代码：https://github.com/facebookresearch/SlowFast

Temporal Pyramid Network for Action Recognition

• 主页：https://decisionforce.github.io/TPN
• 论文：https://arxiv.org/abs/2004.03548

• 代码：https://github.com/decisionforce/TPN

  基于骨架的动作识别

  Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition

• 论文：https://arxiv.org/abs/2003.14111

• 代码：https://github.com/kenziyuliu/ms-g3d

  人群计数

  深度估计

  BiFuse: Monocular 360◦ Depth Estimation via Bi-Projection Fusion

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_BiFuse_Monocular_360_Depth_Estimation_via_Bi-Projection_Fusion_CVPR_2020_paper.pdf

• 代码：https://github.com/Yeh-yu-hsuan/BiFuse

Focus on defocus: bridging the synthetic to real domain gap for depth estimation

• 论文：https://arxiv.org/abs/2005.09623
• 代码：https://github.com/dvl-tum/defocus-net

Bi3D: Stereo Depth Estimation via Binary Classifications

• 论文：https://arxiv.org/abs/2005.07274
• 代码：https://github.com/NVlabs/Bi3D

AANet: Adaptive Aggregation Network for Efficient Stereo Matching

• 论文：https://arxiv.org/abs/2004.09548
• 代码：https://github.com/haofeixu/aanet

Towards Better Generalization: Joint Depth-Pose Learning without PoseNet

• 论文：https://github.com/B1ueber2y/TrianFlow

• 代码：https://github.com/B1ueber2y/TrianFlow

  单目深度估计

  On the uncertainty of self-supervised monocular depth estimation

• 论文：https://arxiv.org/abs/2005.06209

• 代码：https://github.com/mattpoggi/mono-uncertainty

3D Packing for Self-Supervised Monocular Depth Estimation

• 论文：https://arxiv.org/abs/1905.02693
• 代码：https://github.com/TRI-ML/packnet-sfm
• Demo视频：https://www.bilibili.com/video/av70562892/

Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation

• 论文：https://arxiv.org/abs/2002.12114

• 代码：https://github.com/yzhao520/ARC

  6D目标姿态估计

  PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/He_PVN3D_A_Deep_Point-Wise_3D_Keypoints_Voting_Network_for_6DoF_CVPR_2020_paper.pdf

• 代码：https://github.com/ethnhe/PVN3D

MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion

• 论文：https://arxiv.org/abs/2004.04336
• 代码：https://github.com/wkentaro/morefusion

EPOS: Estimating 6D Pose of Objects with Symmetries
主页：http://cmp.felk.cvut.cz/epos
论文：https://arxiv.org/abs/2004.00605
G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features

• 论文：https://arxiv.org/abs/2003.11089

• 代码：https://github.com/DC1991/G2L_Net

  手势估计

  HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation

• 论文：https://arxiv.org/abs/2004.00060

• 主页：http://vision.sice.indiana.edu/projects/hopenet

Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data

• 论文：https://arxiv.org/abs/2003.09572

• 代码：https://github.com/CalciferZh/minimal-hand

  显著性检测

  JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

• 论文：https://arxiv.org/abs/2004.08515

• 代码：https://github.com/kerenfu/JLDCF/

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

• 主页：http://dpfan.net/d3netbenchmark/
• 论文：https://arxiv.org/abs/2004.05763

• 代码：https://github.com/JingZhang617/UCNet

  去噪

  A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising

• 论文：https://arxiv.org/abs/2003.12751

• 代码：https://github.com/Vandermode/NoiseModel

CycleISP: Real Image Restoration via Improved Data Synthesis

• 论文：https://arxiv.org/abs/2003.07761

• 代码：https://github.com/swz30/CycleISP

  去雨

  Multi-Scale Progressive Fusion Network for Single Image Deraining

• 论文：https://arxiv.org/abs/2003.10985

• 代码：https://github.com/kuihua/MSPFN

Detail-recovery Image Deraining via Context Aggregation Networks

• 论文：https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_Detail-recovery_Image_Deraining_via_Context_Aggregation_Networks_CVPR_2020_paper.html

• 代码：https://github.com/Dengsgithub/DRD-Net

  去模糊

  视频去模糊

  Cascaded Deep Video Deblurring Using Temporal Sharpness Prior

• 主页：https://csbhr.github.io/projects/cdvd-tsp/index.html

• 论文：https://arxiv.org/abs/2004.02501

• 代码：https://github.com/csbhr/CDVD-TSP

  去雾

  Domain Adaptation for Image Dehazing

• 论文：https://arxiv.org/abs/2005.04668

• 代码：https://github.com/HUSTSYJ/DA_dahazing

Multi-Scale Boosted Dehazing Network with Dense Feature Fusion

• 论文：https://arxiv.org/abs/2004.13388

• 代码：https://github.com/BookerDeWitt/MSBDN-DFF

  特征点检测与描述

  ASLFeat: Learning Local Features of Accurate Shape and Localization

• 论文：https://arxiv.org/abs/2003.10071

• 代码：https://github.com/lzx551402/aslfeat

  视觉问答(VQA)

  VC R-CNN：Visual Commonsense R-CNN

• 论文：https://arxiv.org/abs/2002.12204

• 代码：https://github.com/Wangt-CN/VC-R-CNN

  视频问答(VideoQA)

  Hierarchical Conditional Relation Networks for Video Question Answering

• 论文：https://arxiv.org/abs/2002.10698

• 代码：https://github.com/thaolmk54/hcrn-videoqa

  视觉语言导航

  Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training

• 论文：https://arxiv.org/abs/2002.10638

• 代码（即将开源）：https://github.com/weituo12321/PREVALENT

  视频压缩

  Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

• 论文：https://arxiv.org/abs/2003.01966

• 代码：https://github.com/RenYang-home/HLVC

  视频插帧

  AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation

• 论文：https://arxiv.org/abs/1907.10244

• 代码：https://github.com/HyeongminLEE/AdaCoF-pytorch

FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Gui_FeatureFlow_Robust_Video_Interpolation_via_Structure-to-Texture_Generation_CVPR_2020_paper.html
• 代码：https://github.com/CM-BF/FeatureFlow

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

• 论文：https://arxiv.org/abs/2002.11616
• 代码：https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020

Space-Time-Aware Multi-Resolution Video Enhancement

• 主页：https://alterzero.github.io/projects/STAR.html
• 论文：http://arxiv.org/abs/2003.13170
• 代码：https://github.com/alterzero/STARnet

Scene-Adaptive Video Frame Interpolation via Meta-Learning

• 论文：https://arxiv.org/abs/2004.00779
• 代码：https://github.com/myungsub/meta-interpolation

Softmax Splatting for Video Frame Interpolation

• 主页：http://sniklaus.com/papers/softsplat
• 论文：https://arxiv.org/abs/2003.05534

• 代码：https://github.com/sniklaus/softmax-splatting

  风格迁移

  Diversified Arbitrary Style Transfer via Deep Feature Perturbation

• 论文：https://arxiv.org/abs/1909.08223

• 代码：https://github.com/EndyWon/Deep-Feature-Perturbation

Collaborative Distillation for Ultra-Resolution Universal Style Transfer

• 论文：https://arxiv.org/abs/2003.08436

• 代码：https://github.com/mingsun-tse/collaborative-distillation

  车道线检测

  Inter-Region Affinity Distillation for Road Marking Segmentation

• 论文：https://arxiv.org/abs/2004.05304

• 代码：https://github.com/cardwing/Codes-for-IntRA-KD

  “人-物”交互(HOT)检测

  PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection

• 论文：https://arxiv.org/abs/1912.12898

• 代码：https://github.com/YueLiao/PPDM

Detailed 2D-3D Joint Representation for Human-Object Interaction

• 论文：https://arxiv.org/abs/2004.08154
• 代码：https://github.com/DirtyHarryLYL/DJ-RN

Cascaded Human-Object Interaction Recognition

• 论文：https://arxiv.org/abs/2003.04262
• 代码：https://github.com/tfzhou/C-HOI

VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions

• 论文：https://arxiv.org/abs/2003.05541

• 代码：https://github.com/ASMIftekhar/VSGNet

  轨迹预测

  The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

• 论文：https://arxiv.org/abs/1912.06445

• 代码：https://github.com/JunweiLiang/Multiverse
• 数据集：https://next.cs.cmu.edu/multiverse/

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

• 论文：https://arxiv.org/abs/2002.11927

• 代码：https://github.com/abduallahmohamed/Social-STGCNN

  运动预测

  Collaborative Motion Prediction via Neural Motion Message Passing

• 论文：https://arxiv.org/abs/2003.06594

• 代码：https://github.com/PhyllisH/NMMP

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps

• 论文：https://arxiv.org/abs/2003.06754

• 代码：https://github.com/pxiangwu/MotionNet

  光流估计

  Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation

• 论文：https://arxiv.org/abs/2003.13045

• 代码：https://github.com/lliuz/ARFlow

  图像检索

  Evade Deep Image Retrieval by Stashing Private Images in the Hash Space

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_Evade_Deep_Image_Retrieval_by_Stashing_Private_Images_in_the_CVPR_2020_paper.html

• 代码：https://github.com/sugarruy/hashstash

  虚拟试衣

  Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content

• 论文：https://arxiv.org/abs/2003.05863

• 代码：https://github.com/switchablenorms/DeepFashion_Try_On

  HDR

  Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline

• 主页：https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR

• 论文下载链接：https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR_/00942.pdf

• 代码：https://github.com/alex04072000/SingleHDR

  对抗样本

  Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction

• 论文：https://openaccess.thecvf.com/content_CVPR_2020/papers/Lu_Enhancing_Cross-Task_Black-Box_Transferability_of_Adversarial_Examples_With_Dispersion_Reduction_CVPR_2020_paper.pdf

• 代码：https://github.com/erbloo/dr_cvpr20

Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance

• 论文：https://arxiv.org/abs/1911.02466

• 代码：https://github.com/ZhengyuZhao/PerC-Adversarial

  三维重建

  Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

• CVPR 2020 Best Paper

• 主页：https://elliottwu.com/projects/unsup3d/
• 论文：https://arxiv.org/abs/1911.11130
• 代码：https://github.com/elliottwu/unsup3d

Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

• 主页：https://shunsukesaito.github.io/PIFuHD/
• 论文：https://arxiv.org/abs/2004.00452
• 代码：https://github.com/facebookresearch/pifuhd
• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Patel_TailorNet_Predicting_Clothing_in_3D_as_a_Function_of_Human_CVPR_2020_paper.pdf
• 代码：https://github.com/chaitanya100100/TailorNet
• 数据集：https://github.com/zycliao/TailorNet_dataset

Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Chibane_Implicit_Functions_in_Feature_Space_for_3D_Shape_Reconstruction_and_CVPR_2020_paper.pdf
• 代码：https://github.com/jchibane/if-net
• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Mir_Learning_to_Transfer_Texture_From_Clothing_Images_to_3D_Humans_CVPR_2020_paper.pdf

• 代码：https://github.com/aymenmir1/pix2surf

  深度补全

  Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
  论文：https://arxiv.org/abs/2006.03349
  代码：https://github.com/abdo-eldesokey/pncnn

  语义场景补全

  3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior

• 论文：https://arxiv.org/abs/2003.14052

• 代码：https://github.com/charlesCXK/TorchSSC

  图像/视频描述

  Syntax-Aware Action Targeting for Video Captioning

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Syntax-Aware_Action_Targeting_for_Video_Captioning_CVPR_2020_paper.pdf

• 代码：https://github.com/SydCaption/SAAT

  线框解析

  Holistically-Attracted Wireframe Parser

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Holistically-Attracted_Wireframe_Parsing_CVPR_2020_paper.html

• 代码：https://github.com/cherubicXN/hawp

  数据集

  OASIS: A Large-Scale Dataset for Single Image 3D in the Wild

• 论文：https://arxiv.org/abs/2007.13215

• 数据集：https://oasis.cs.princeton.edu/

STEFANN: Scene Text Editor using Font Adaptive Neural Network

• 主页：https://prasunroy.github.io/stefann/
• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Roy_STEFANN_Scene_Text_Editor_Using_Font_Adaptive_Neural_Network_CVPR_2020_paper.html
• 代码：https://github.com/prasunroy/stefann
• 数据集：https://drive.google.com/open?id=1sEDiX_jORh2X-HSzUnjIyZr-G9LJIw1k

Interactive Object Segmentation with Inside-Outside Guidance

• 论文下载链接：http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Interactive_Object_Segmentation_With_Inside-Outside_Guidance_CVPR_2020_paper.pdf
• 代码：https://github.com/shiyinzhang/Inside-Outside-Guidance
• 数据集：https://github.com/shiyinzhang/Pixel-ImageNet

Video Panoptic Segmentation

• 论文：https://arxiv.org/abs/2006.11339
• 代码：https://github.com/mcahny/vps
• 数据集：https://www.dropbox.com/s/ecem4kq0fdkver4/cityscapes-vps-dataset-1.0.zip?dl=0

FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Li_FSS-1000_A_1000-Class_Dataset_for_Few-Shot_Segmentation_CVPR_2020_paper.html
• 代码：https://github.com/HKUSTCV/FSS-1000
• 数据集：https://github.com/HKUSTCV/FSS-1000

3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset

• 主页：https://vap.aau.dk/3d-zef/
• 论文：https://arxiv.org/abs/2006.08466
• 代码：https://bitbucket.org/aauvap/3d-zef/src/master/
• 数据集：https://motchallenge.net/data/3D-ZeF20

TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Patel_TailorNet_Predicting_Clothing_in_3D_as_a_Function_of_Human_CVPR_2020_paper.pdf
• 代码：https://github.com/chaitanya100100/TailorNet
• 数据集：https://github.com/zycliao/TailorNet_dataset

Oops! Predicting Unintentional Action in Video

• 主页：https://oops.cs.columbia.edu/
• 论文：https://arxiv.org/abs/1911.11206
• 代码：https://github.com/cvlab-columbia/oops
• 数据集：https://oops.cs.columbia.edu/data

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

• 论文：https://arxiv.org/abs/1912.06445
• 代码：https://github.com/JunweiLiang/Multiverse
• 数据集：https://next.cs.cmu.edu/multiverse/

Open Compound Domain Adaptation

• 主页：https://liuziwei7.github.io/projects/CompoundDomain.html
• 数据集：https://drive.google.com/drive/folders/1_uNTF8RdvhS_sqVTnYx17hEOQpefmE2r?usp=sharing
• 论文：https://arxiv.org/abs/1909.03403
• 代码：https://github.com/zhmiao/OpenCompoundDomainAdaptation-OCDA

Intra- and Inter-Action Understanding via Temporal Action Parsing

• 论文：https://arxiv.org/abs/2005.10229
• 主页和数据集：https://sdolivia.github.io/TAPOS/

Dynamic Refinement Network for Oriented and Densely Packed Object Detection

• 论文下载链接：https://arxiv.org/abs/2005.09973
• 代码和数据集：https://github.com/Anymake/DRN_CVPR2020

COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification

• 论文：https://arxiv.org/abs/2005.07862
• 数据集：暂无

KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations

• 论文：https://arxiv.org/abs/2002.12687
• 数据集：https://github.com/qq456cvb/KeypointNet

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

• 论文：http://vladlen.info/papers/MSeg.pdf
• 代码：https://github.com/mseg-dataset/mseg-api
• 数据集：https://github.com/mseg-dataset/mseg-semantic

AvatarMe: Realistically Renderable 3D Facial Reconstruction “in-the-wild”

• 论文：https://arxiv.org/abs/2003.13845
• 数据集：https://github.com/lattas/AvatarMe

Learning to Autofocus

• 论文：https://arxiv.org/abs/2004.12260
• 数据集：暂无

FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

• 论文：https://arxiv.org/abs/2003.13989
• 代码：https://github.com/zhuhao-nju/facescape

Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data

• 论文下载链接：https://arxiv.org/abs/2004.01166
• 代码：https://github.com/Healthcare-Robotics/bodies-at-rest
• 数据集：https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KOA4ML

FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding

• 主页：https://sdolivia.github.io/FineGym/
• 论文：https://arxiv.org/abs/2004.06704

A Local-to-Global Approach to Multi-modal Movie Scene Segmentation

• 主页：https://anyirao.com/projects/SceneSeg.html
• 论文下载链接：https://arxiv.org/abs/2004.02678
• 代码：https://github.com/AnyiRao/SceneSeg

Deep Homography Estimation for Dynamic Scenes

• 论文：https://arxiv.org/abs/2004.02132
• 数据集：https://github.com/lcmhoang/hmg-dynamics

Assessing Image Quality Issues for Real-World Problems

• 主页：https://vizwiz.org/tasks-and-datasets/image-quality-issues/
• 论文：https://arxiv.org/abs/2003.12511

UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World

• 论文：https://arxiv.org/abs/2003.10608
• 代码和数据集：https://github.com/Jyouhou/UnrealText/

PANDA: A Gigapixel-level Human-centric Video Dataset

• 论文：https://arxiv.org/abs/2003.04852
• 数据集：http://www.panda-dataset.com/

IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

• 论文：https://arxiv.org/abs/2003.02920
• 数据集：https://github.com/intra3d2019/IntrA

Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS

• 论文：https://arxiv.org/abs/2003.03972

• 数据集：暂无

  其他

  CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus

• 论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.html

• 代码：https://github.com/fkluger/consac

Learning to Learn Single Domain Generalization

• 论文：https://arxiv.org/abs/2003.13216
• 代码：https://github.com/joffery/M-ADA

Open Compound Domain Adaptation

• 主页：https://liuziwei7.github.io/projects/CompoundDomain.html
• 数据集：https://drive.google.com/drive/folders/1_uNTF8RdvhS_sqVTnYx17hEOQpefmE2r?usp=sharing
• 论文：https://arxiv.org/abs/1909.03403
• 代码：https://github.com/zhmiao/OpenCompoundDomainAdaptation-OCDA

Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision

• 论文：http://www.cvlibs.net/publications/Niemeyer2020CVPR.pdf
• 代码：https://github.com/autonomousvision/differentiable_volumetric_rendering

QEBA: Query-Efficient Boundary-Based Blackbox Attack

• 论文：https://arxiv.org/abs/2005.14137
• 代码：https://github.com/AI-secure/QEBA

Equalization Loss for Long-Tailed Object Recognition

• 论文：https://arxiv.org/abs/2003.05176
• 代码：https://github.com/tztztztztz/eql.detectron2

Instance-aware Image Colorization

• 主页：https://ericsujw.github.io/InstColorization/
• 论文：https://arxiv.org/abs/2005.10825
• 代码：https://github.com/ericsujw/InstColorization

Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting

• 论文：https://arxiv.org/abs/2005.09704
• 代码：https://github.com/Atlas200dk/sample-imageinpainting-HiFill

Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching

• 论文：https://arxiv.org/abs/2005.03860
• 代码：https://github.com/shiyujiao/cross_view_localization_DSM

Epipolar Transformers

• 论文：https://arxiv.org/abs/2005.04551
• 代码：https://github.com/yihui-he/epipolar-transformers

Bringing Old Photos Back to Life

• 主页：http://raywzy.com/Old_Photo/
• 论文：https://arxiv.org/abs/2004.09484

MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask

• 论文：https://arxiv.org/abs/2003.10955
• 代码：https://github.com/microsoft/MaskFlownet

Self-Supervised Viewpoint Learning from Image Collections

• 论文：https://arxiv.org/abs/2004.01793
• 论文2：https://research.nvidia.com/sites/default/files/pubs/2020-03_Self-Supervised-Viewpoint-Learning/SSV-CVPR2020.pdf
• 代码：https://github.com/NVlabs/SSV

Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations

• Oral
• 论文：https://arxiv.org/abs/2003.12237
• 代码：https://github.com/cuishuhao/BNM

Towards Learning Structure via Consensus for Face Segmentation and Parsing

• 论文：https://arxiv.org/abs/1911.00957
• 代码：https://github.com/isi-vista/structure_via_consensus

Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging

• Oral
• 论文：https://arxiv.org/abs/2003.13654
• 代码：https://github.com/liuyang12/PnP-SCI

Lightweight Photometric Stereo for Facial Details Recovery

• 论文：https://arxiv.org/abs/2003.12307
• 代码：https://github.com/Juyong/FacePSNet

Footprints and Free Space from a Single Color Image

• 论文：https://arxiv.org/abs/2004.06376
• 代码：https://github.com/nianticlabs/footprints

Self-Supervised Monocular Scene Flow Estimation

• 论文：https://arxiv.org/abs/2004.04143
• 代码：https://github.com/visinf/self-mono-sf

Quasi-Newton Solver for Robust Non-Rigid Registration

• 论文：https://arxiv.org/abs/2004.04322
• 代码：https://github.com/Juyong/Fast_RNRR

A Local-to-Global Approach to Multi-modal Movie Scene Segmentation

• 主页：https://anyirao.com/projects/SceneSeg.html
• 论文下载链接：https://arxiv.org/abs/2004.02678
• 代码：https://github.com/AnyiRao/SceneSeg

DeepFLASH: An Efficient Network for Learning-based Medical Image Registration

• 论文：https://arxiv.org/abs/2004.02097
• 代码：https://github.com/jw4hv/deepflash

Self-Supervised Scene De-occlusion

• 主页：https://xiaohangzhan.github.io/projects/deocclusion/
• 论文：https://arxiv.org/abs/2004.02788
• 代码：https://github.com/XiaohangZhan/deocclusion

Polarized Reflection Removal with Perfect Alignment in the Wild

• 主页：https://leichenyang.weebly.com/project-polarized.html
• 代码：https://github.com/ChenyangLEI/CVPR2020-Polarized-Reflection-Removal-with-Perfect-Alignment

Background Matting: The World is Your Green Screen

• 论文：https://arxiv.org/abs/2004.00626
• 代码：http://github.com/senguptaumd/Background-Matting

What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective

• 论文：https://arxiv.org/abs/2003.11241
• 代码：https://github.com/ZhangLi-CS/GCP_Optimization

Look-into-Object: Self-supervised Structure Modeling for Object Recognition

• 论文：暂无
• 代码：https://github.com/JDAI-CV/LIO

Video Object Grounding using Semantic Roles in Language Description

• 论文：https://arxiv.org/abs/2003.10606
• 代码：https://github.com/TheShadow29/vognet-pytorch

Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives

• 论文：https://arxiv.org/abs/2003.10739
• 代码：https://github.com/d-li14/DHM

SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization

• 论文：http://www.cs.umd.edu/~yuejiang/papers/SDFDiff.pdf
• 代码：https://github.com/YueJiang-nj/CVPR2020-SDFDiff

On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location

• 论文：https://arxiv.org/abs/2003.07064
• 代码：https://github.com/oskyhn/CNNs-Without-Borders

GhostNet: More Features from Cheap Operations

• 论文：https://arxiv.org/abs/1911.11907
• 代码：https://github.com/iamhankai/ghostnet

AdderNet: Do We Really Need Multiplications in Deep Learning?

• 论文：https://arxiv.org/abs/1912.13200
• 代码：https://github.com/huawei-noah/AdderNet

Deep Image Harmonization via Domain Verification

• 论文：https://arxiv.org/abs/1911.13239
• 代码：https://github.com/bcmi/Image_Harmonization_Datasets

Blurry Video Frame Interpolation

• 论文：https://arxiv.org/abs/2002.12259
• 代码：https://github.com/laomao0/BIN

Extremely Dense Point Correspondences using a Learned Feature Descriptor

• 论文：https://arxiv.org/abs/2003.00619
• 代码：https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch

Filter Grafting for Deep Neural Networks

• 论文：https://arxiv.org/abs/2001.05868
• 代码：https://github.com/fxmeng/filter-grafting
• 论文解读：https://www.zhihu.com/question/372070853/answer/1041569335

Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation

• 论文：https://arxiv.org/abs/2003.02824
• 代码：https://github.com/cmhungsteve/SSTDA

Detecting Attended Visual Targets in Video

• 论文：https://arxiv.org/abs/2003.02501
• 代码：https://github.com/ejcgt/attention-target-detection

Deep Image Spatial Transformation for Person Image Generation

• 论文：https://arxiv.org/abs/2003.00696
• 代码：https://github.com/RenYurui/Global-Flow-Local-Attention

Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

• 论文：https://arxiv.org/abs/2003.01455
• 代码：https://github.com/bbrattoli/ZeroShotVideoClassification

https://github.com/charlesCXK/3D-SketchAware-SSC
https://github.com/Anonymous20192020/Anonymous_CVPR5767
https://github.com/avirambh/ScopeFlow
https://github.com/csbhr/CDVD-TSP
https://github.com/ymcidence/TBH
https://github.com/yaoyao-liu/mnemonics
https://github.com/meder411/Tangent-Images
https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch
https://github.com/sjmoran/deep_local_parametric_filters
https://github.com/charlesCXK/3D-SketchAware-SSC
https://github.com/bermanmaxim/AOWS
https://github.com/dc3ea9f/look-into-object

不确定中没中

FADNet: A Fast and Accurate Network for Disparity Estimation

• 论文：还没出来
• 代码：https://github.com/HKBU-HPML/FADNet

https://github.com/rFID-submit/RandomFID：不确定中没中
https://github.com/JackSyu/AE-MSR：不确定中没中
https://github.com/fastconvnets/cvpr2020：不确定中没中
https://github.com/aimagelab/meshed-memory-transformer：不确定中没中
https://github.com/TWSFar/CRGNet：不确定中没中
https://github.com/CVPR-2020/CDARTS：不确定中没中
https://github.com/anucvml/ddn-cvprw2020：不确定中没中
https://github.com/dl-model-recommend/model-trust：不确定中没中
https://github.com/apratimbhattacharyya18/CVPR-2020-Corr-Prior：不确定中没中
https://github.com/onetcvpr/O-Net：不确定中没中
https://github.com/502463708/Microcalcification_Detection：不确定中没中
https://github.com/anonymous-for-review/cvpr-2020-deep-smoke-machine：不确定中没中
https://github.com/anonymous-for-review/cvpr-2020-smoke-recognition-dataset：不确定中没中
https://github.com/cvpr-nonrigid/dataset：不确定中没中
https://github.com/theFool32/PPBA：不确定中没中
https://github.com/Realtime-Action-Recognition/Realtime-Action-Recognition

参考：

1. https://bbs.cvmart.net/articles/2964 CVPR 2020 论文大盘点-图像与视频检索篇