Xepsiloner/CVPR2021-Papers-with-Code

CVPR 2021 论文和开源项目合集

CVPR 2021 论文和开源项目合集(Papers with Code)

CVPR 2021 论文和开源项目合集(papers with code)！

CVPR 2021 收录列表：http://cvpr2021.thecvf.com/sites/default/files/2021-03/accepted_paper_ids.txt

注1：欢迎各位大佬提交issue，分享CVPR 2021论文和开源项目！

注2：关于往年CV顶会论文以及其他优质CV论文和大盘点，详见： https://github.com/amusi/daily-paper-computer-vision

CVPR 2021 中奖群已成立！已经收录的同学，可以添加微信：CVer9999，请备注：CVPR2021已收录+姓名+学校/公司名称！一定要根据格式申请，可以拉你进群沟通开会等事宜。

【CVPR 2021 论文开源目录】

• Backbone
• NAS
• GAN
• Visual Transformer
• Regularization
• 无监督/自监督(Self-Supervised)
• 半监督(Semi-Supervised)
• 2D/遥感目标检测(Object Detection)
• 单/多目标跟踪(Object Tracking)
• 实例分割(Instance Segmentation)
• 全景分割(Panoptic Segmentation)
• 医学图像分割(Medical Image Segmentation)
• 交互式视频目标分割(Interactive-Video-Object-Segmentation)
• 视频理解/行为识别(Video Understanding)
• 人脸识别(Face Recognition)
• 人脸检测(Face Detection)
• 人脸活体检测(Face Anti-Spoofing)
• Deepfake检测(Deepfake Detection)
• 人脸年龄估计(Age-Estimation)
• 人体解析(Human Parsing)
• 2D/3D人体姿态估计(2D/3D Human Pose Estimation)
• 场景文本识别(Scene Text Recognition)
• 模型压缩/剪枝/量化
• 超分辨率(Super-Resolution)
• 图像恢复(Image Restoration)
• 反光去除(Reflection Removal)
• 3D目标检测(3D Object Detection)
• 3D语义分割(3D Semantic Segmentation)
• 3D目标跟踪(3D Object Tracking)
• 3D点云配准(3D Point Cloud Registration)
• 3D点云补全(3D-Point-Cloud-Completion)
• 6D位姿估计(6D Pose Estimation)
• 相机姿态估计(Camera Pose Estimation)
• 深度估计(Depth Estimation)
• 对抗样本(Adversarial-Examples)
• 图像检索(Image Retrieval)
• 视频检索(Video Retrieval)
• Zero-Shot Learning
• 联邦学习(Federated Learning)
• 视频插帧(Video Frame Interpolation)
• 视觉推理(Visual Reasoning)
• 视图合成(Visual Synthesis)
• Domain Generalization
• "人-物"交互(HOI)检测
• 阴影去除(Shadow Removal)
• 虚拟试衣
• 数据集(Datasets)
• 其他(Others)
• 待添加(TODO)
• 不确定中没中(Not Sure)

Backbone

ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network

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

Involution: Inverting the Inherence of Convolution for Visual Recognition

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

Coordinate Attention for Efficient Mobile Network Design

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

Inception Convolution with Efficient Dilation Search

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

RepVGG: Making VGG-style ConvNets Great Again

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

NAS

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

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

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

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

Inception Convolution with Efficient Dilation Search

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

GAN

HumanGAN: A Generative Model of Humans Images

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

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

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

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

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

Training Generative Adversarial Networks in One Stage

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

Closed-Form Factorization of Latent Semantics in GANs

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

Anycost GANs for Interactive Image Synthesis and Editing

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

Image-to-image Translation via Hierarchical Style Disentanglement

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

Visual Transformer

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking

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

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

Pre-Trained Image Processing Transformer

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

End-to-End Video Instance Segmentation with Transformers

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

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

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

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

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

Transformer Interpretability Beyond Attention Visualization

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

Regularization

Regularizing Neural Networks via Adversarial Model Perturbation

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

无监督/自监督(Un/Self-Supervised)

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

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

Spatially Consistent Representation Learning

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

VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples

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

Exploring Simple Siamese Representation Learning

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

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

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

半监督学习(Semi-Supervised )

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

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

Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

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

2D/遥感目标检测(Object Detection)

2D目标检测

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

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

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

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

YOLOF：You Only Look One-level Feature

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

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

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

General Instance Distillation for Object Detection

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

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

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

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

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

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

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

Multiple Instance Active Learning for Object Detection

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

Towards Open World Object Detection

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

Few-Shot目标检测

Few-Shot Object Detection via Contrastive Proposal Encoding

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

旋转目标检测

ReDet: A Rotation-equivariant Detector for Aerial Object Detection

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

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

单/多目标跟踪(Object Tracking)

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking

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

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

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

实例分割(Instance Segmentation)

End-to-End Video Instance Segmentation with Transformers

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

Zero-shot instance segmentation（Not Sure）

• Paper: None
• Code: https://github.com/CVPR2021-pape-id-1395/CVPR2021-paper-id-1395

全景分割(Panoptic Segmentation)

Fully Convolutional Networks for Panoptic Segmentation

• Paper: https://arxiv.org/abs/2012.00720

• Code: https://github.com/yanwei-li/PanopticFCN

Cross-View Regularization for Domain Adaptive Panoptic Segmentation

• Paper: https://arxiv.org/abs/2103.02584
• Code: None

医学图像分割

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

交互式视频目标分割(Interactive-Video-Object-Segmentation)

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

视频理解/行为识别(Video Understanding)

ACTION-Net: Multipath Excitation for Action Recognition

• Paper: https://arxiv.org/abs/2103.07372
• Code: https://github.com/V-Sense/ACTION-Net

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

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

TDN: Temporal Difference Networks for Efficient Action Recognition

• Paper: https://arxiv.org/abs/2012.10071
• Code: https://github.com/MCG-NJU/TDN

人脸识别(Face Recognition)

MagFace: A Universal Representation for Face Recognition and Quality Assessment

• Paper(Oral): https://arxiv.org/abs/2103.06627
• Code: https://github.com/IrvingMeng/MagFace

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

• Homepage: https://www.face-benchmark.org/
• Paper: https://arxiv.org/abs/2103.04098
• Dataset: https://www.face-benchmark.org/

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

• Paper(Oral): https://arxiv.org/abs/2103.01520
• Code: https://github.com/Hzzone/MTLFace
• Dataset: https://github.com/Hzzone/MTLFace

人脸检测(Face Detection)

CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement

• Paper: https://arxiv.org/abs/2103.07017
• Code: None

人脸活体检测(Face Anti-Spoofing)

Cross Modal Focal Loss for RGBD Face Anti-Spoofing

• Paper: https://arxiv.org/abs/2103.00948
• Code: None

Deepfake检测(Deepfake Detection)

Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

• Paper：https://arxiv.org/abs/2103.01856
• Code: None

Multi-attentional Deepfake Detection

• Paper：https://arxiv.org/abs/2103.02406
• Code: None

人脸年龄估计(Age Estimation)

PML: Progressive Margin Loss for Long-tailed Age Classification

• Paper: https://arxiv.org/abs/2103.02140
• Code: None

人体解析(Human Parsing)

Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing

• Paper: https://arxiv.org/abs/2103.04570
• Code: https://github.com/tfzhou/MG-HumanParsing

2D/3D人体姿态估计(2D/3D Human Pose Estimation)

2D 人体姿态估计

DCPose: Deep Dual Consecutive Network for Human Pose Estimation

• Paper: https://arxiv.org/abs/2103.07254
• Code: https://github.com/Pose-Group/DCPose

3D 人体姿态估计

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

场景文本识别(Scene Text Recognition)

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

• Paper: https://arxiv.org/abs/2103.06495
• Code: https://github.com/FangShancheng/ABINet

模型压缩/剪枝/量化

模型量化

Learnable Companding Quantization for Accurate Low-bit Neural Networks

• Paper: https://arxiv.org/abs/2103.07156
• Code: None

超分辨率(Super-Resolution)

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

图像恢复(Image Restoration)

Multi-Stage Progressive Image Restoration

• Paper: https://arxiv.org/abs/2102.02808
• Code: https://github.com/swz30/MPRNet

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目标检测(3D Object Detection)

SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud

• Paper: None
• Code: https://github.com/Vegeta2020/SE-SSD

Center-based 3D Object Detection and Tracking

• Paper: https://arxiv.org/abs/2006.11275
• Code: https://github.com/tianweiy/CenterPoint

Categorical Depth Distribution Network for Monocular 3D Object Detection

• Paper: https://arxiv.org/abs/2103.01100
• Code: None

3D语义分割(3D Semantic Segmentation)

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目标跟踪(3D Object Trancking)

Center-based 3D Object Detection and Tracking

• Paper: https://arxiv.org/abs/2006.11275
• Code: https://github.com/tianweiy/CenterPoint

3D点云配准(3D Point Cloud Registration)

PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

• Paper: https://arxiv.org/abs/2103.05465
• Code: https://github.com/XuyangBai/PointDSC

PREDATOR: Registration of 3D Point Clouds with Low Overlap

• Paper: https://arxiv.org/abs/2011.13005
• Code: https://github.com/ShengyuH/OverlapPredator

3D点云补全(3D Point Cloud Completion)

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion

• Paper: https://arxiv.org/abs/2103.02535
• Code: None

6D位姿估计(6D Pose Estimation)

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

• Paper: http://arxiv.org/abs/2102.12145
• code: https://git.io/GDR-Net

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

• Paper: https://arxiv.org/abs/2103.02242
• Code: https://github.com/ethnhe/FFB6D

相机姿态估计

Back to the Feature: Learning Robust Camera Localization from Pixels to Pose

• Paper: https://arxiv.org/abs/2103.09213
• Code: https://github.com/cvg/pixloc

深度估计

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

对抗样本

Natural Adversarial Examples

• Paper: https://arxiv.org/abs/1907.07174
• Code: https://github.com/hendrycks/natural-adv-examples

图像检索(Image Retrieval)

QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval

• Paper: https://arxiv.org/abs/2103.02927
• Code: None

视频检索(Video Retrieval)

On Semantic Similarity in Video Retrieval

• Paper: https://arxiv.org/abs/2103.10095

• Homepage: https://mwray.github.io/SSVR/

• Code: https://github.com/mwray/Semantic-Video-Retrieval

Zero-Shot Learning

Counterfactual Zero-Shot and Open-Set Visual Recognition

• Paper: https://arxiv.org/abs/2103.00887
• Code: https://github.com/yue-zhongqi/gcm-cf

联邦学习(Federated Learning)

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

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

视频插帧(Video Frame Interpolation)

FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation

• Homepage: https://tarun005.github.io/FLAVR/

• Paper: https://arxiv.org/abs/2012.08512

• Code: https://github.com/tarun005/FLAVR

视觉推理(Visual Reasoning)

Transformation Driven Visual Reasoning

• homepage: https://hongxin2019.github.io/TVR/
• Paper: https://arxiv.org/abs/2011.13160
• Code: https://github.com/hughplay/TVR

视图合成(View Synthesis)

NeX: Real-time View Synthesis with Neural Basis Expansion

• Homepage: https://nex-mpi.github.io/
• Paper(Oral): https://arxiv.org/abs/2103.05606

DomainGeneralization

FSDR: Frequency Space Domain Randomization for Domain Generalization

• Paper: https://arxiv.org/abs/2103.02370
• Code: None

"人-物"交互(HOI)检测

Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information

• Paper: https://arxiv.org/abs/2103.05399
• Code: https://github.com/hitachi-rd-cv/qpic

Reformulating HOI Detection as Adaptive Set Prediction

• Paper: https://arxiv.org/abs/2103.05983
• Code: https://github.com/yoyomimi/AS-Net

Detecting Human-Object Interaction via Fabricated Compositional Learning

• Paper: https://arxiv.org/abs/2103.08214
• Code: https://github.com/zhihou7/FCL

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

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

阴影去除(Shadow Removal)

Auto-Exposure Fusion for Single-Image Shadow Removal

• Paper: https://arxiv.org/abs/2103.01255
• Code: https://github.com/tsingqguo/exposure-fusion-shadow-removal

虚拟换衣(Virtual Try-On)

Parser-Free Virtual Try-on via Distilling Appearance Flows

基于外观流蒸馏的无需人体解析的虚拟换装

• Paper: https://arxiv.org/abs/2103.04559
• Code: https://github.com/geyuying/PF-AFN

数据集(Datasets)

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)

Knowledge Evolution in Neural Networks

• Paper(Oral): https://arxiv.org/abs/2103.05152
• Code: https://github.com/ahmdtaha/knowledge_evolution

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

• Paper: https://arxiv.org/abs/2103.02148
• Code: https://github.com/guopengf/FLMRCM

SGP: Self-supervised Geometric Perception

• Oral

• Paper: https://arxiv.org/abs/2103.03114

• Code: https://github.com/theNded/SGP

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

• Paper: https://arxiv.org/abs/2103.02148
• Code: https://github.com/guopengf/FLMRCM

Diffusion Probabilistic Models for 3D Point Cloud Generation

• Paper: https://arxiv.org/abs/2103.01458
• Code: https://github.com/luost26/diffusion-point-cloud

Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

• Paper: https://arxiv.org/abs/2012.02206

• Code: https://github.com/daveredrum/Scan2Cap

• Dataset: https://github.com/daveredrum/ScanRefer

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

• Paper: https://arxiv.org/abs/2103.01353

• Code: http://rl.uni-freiburg.de/research/multimodal-distill

• Dataset: http://rl.uni-freiburg.de/research/multimodal-distill

待添加(TODO)

• 重磅！腾讯优图20篇论文入选CVPR 2021
• MePro团队三篇论文被CVPR 2021接收

不确定中没中(Not Sure)

CT Film Recovery via Disentangling Geometric Deformation and Photometric Degradation: Simulated Datasets and Deep Models

• Paper: none
• Code: https://github.com/transcendentsky/Film-Recovery

Toward Explainable Reflection Removal with Distilling and Model Uncertainty

• Paper: none
• Code: https://github.com/ytpeng-aimlab/CVPR-2021-Toward-Explainable-Reflection-Removal-with-Distilling-and-Model-Uncertainty

DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation

• Paper: none
• Code: https://github.com/lhaippp/DeepOIS

Exploring Adversarial Fake Images on Face Manifold

• Paper: none
• Code: https://github.com/ldz666666/Style-atk

Uncertainty-Aware Semi-Supervised Crowd Counting via Consistency-Regularized Surrogate Task

• Paper: none
• Code: https://github.com/yandamengdanai/Uncertainty-Aware-Semi-Supervised-Crowd-Counting-via-Consistency-Regularized-Surrogate-Task

Temporal Contrastive Graph for Self-supervised Video Representation Learning

• Paper: none
• Code: https://github.com/YangLiu9208/TCG

Boosting Monocular Depth Estimation Models to High-Resolution via Context-Aware Patching

• Paper: none
• Code: https://github.com/ouranonymouscvpr/cvpr2021_ouranonymouscvpr

Fast and Memory-Efficient Compact Bilinear Pooling

• Paper: none
• Code: https://github.com/cvpr2021kp2/cvpr2021kp2

Identification of Empty Shelves in Supermarkets using Domain-inspired Features with Structural Support Vector Machine

• Paper: none
• Code: https://github.com/gapDetection/cvpr2021

Estimating A Child's Growth Potential From Cephalometric X-Ray Image via Morphology-Aware Interactive Keypoint Estimation

• Paper: none
• Code: https://github.com/interactivekeypoint2020/Morph

https://github.com/ShaoQiangShen/CVPR2021

https://github.com/gillesflash/CVPR2021

https://github.com/anonymous-submission1991/BaLeNAS

https://github.com/cvpr2021dcb/cvpr2021dcb

https://github.com/anonymousauthorCV/CVPR2021_PaperID_8578

https://github.com/AldrichZeng/FreqPrune

https://github.com/Anonymous-AdvCAM/Anonymous-AdvCAM

https://github.com/ddfss/datadrive-fss