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CVPR 2022 论文和开源项目合集

机器学习AI算法工程

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2022-03-28 05:12

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【CVPR 2022 论文开源目录】

  • Backbone

  • CLIP

  • GAN

  • NAS

  • NeRF

  • Visual Transformer

  • 视觉和语言(Vision-Language)

  • 自监督学习(Self-supervised Learning)

  • 数据增强(Data Augmentation)

  • 目标检测(Object Detection)

  • 目标跟踪(Visual Tracking)

  • 语义分割(Semantic Segmentation)

  • 实例分割(Instance Segmentation)

  • 小样本分割(Few-Shot Segmentation)

  • 视频理解(Video Understanding)

  • 图像编辑(Image Editing)

  • Low-level Vision

  • 超分辨率(Super-Resolution)

  • 3D点云(3D Point Cloud)

  • 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人体姿态估计(3D Human Pose Estimation)

  • 3D语义场景补全(3D Semantic Scene Completion)

  • 3D重建(3D Reconstruction)

  • 伪装物体检测(Camouflaged Object Detection)

  • 深度估计(Depth Estimation)

  • 立体匹配(Stereo Matching)

  • 车道线检测(Lane Detection)

  • 图像修复(Image Inpainting)

  • 人群计数(Crowd Counting)

  • 医学图像(Medical Image)

  • 场景图生成(Scene Graph Generation)

  • 弱监督物体检测(Weakly Supervised Object Localization)

  • 高光谱图像重建(Hyperspectral Image Reconstruction)

  • 水印(Watermarking)

  • 数据集(Datasets)

  • 新任务(New Tasks)

  • 其他(Others)


Backbone

A ConvNet for the 2020s

Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

MPViT : Multi-Path Vision Transformer for Dense Prediction


CLIP

HairCLIP: Design Your Hair by Text and Reference Image

  • Paper: https://arxiv.org/abs/2112.05142

  • Code: https://github.com/wty-ustc/HairCLIP

PointCLIP: Point Cloud Understanding by CLIP

  • Paper: https://arxiv.org/abs/2112.02413

  • Code: https://github.com/ZrrSkywalker/PointCLIP

Blended Diffusion for Text-driven Editing of Natural Images

  • Paper: https://arxiv.org/abs/2111.14818

  • Code: https://github.com/omriav/blended-diffusion


GAN

SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing

  • Homepage: https://semanticstylegan.github.io/

  • Paper: https://arxiv.org/abs/2112.02236

  • Demo: https://semanticstylegan.github.io/videos/demo.mp4

Style Transformer for Image Inversion and Editing

  • Paper: https://arxiv.org/abs/2203.07932

  • Code: https://github.com/sapphire497/style-transformer


NAS

β-DARTS: Beta-Decay Regularization for Differentiable Architecture Search

  • Paper: https://arxiv.org/abs/2203.01665

  • Code: https://github.com/Sunshine-Ye/Beta-DARTS

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

  • Paper: https://arxiv.org/abs/2111.15362

  • Code: None


NeRF

Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

  • Homepage: https://jonbarron.info/mipnerf360/

  • Paper: https://arxiv.org/abs/2111.12077

  • Demo: https://youtu.be/YStDS2-Ln1s

Point-NeRF: Point-based Neural Radiance Fields

  • Homepage: https://xharlie.github.io/projects/project_sites/pointnerf/

  • Paper: https://arxiv.org/abs/2201.08845

  • Code: https://github.com/Xharlie/point-nerf

NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images

  • Paper: https://arxiv.org/abs/2111.13679

  • Homepage: https://bmild.github.io/rawnerf/

  • Demo: https://www.youtube.com/watch?v=JtBS4KBcKVc

Urban Radiance Fields

  • Homepage: https://urban-radiance-fields.github.io/

  • Paper: https://arxiv.org/abs/2111.14643

  • Demo: https://youtu.be/qGlq5DZT6uc

Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translation

  • Paper: https://arxiv.org/abs/2202.13162

  • Code: https://github.com/HexagonPrime/Pix2NeRF

HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video

  • Homepage: https://grail.cs.washington.edu/projects/humannerf/

  • Paper: https://arxiv.org/abs/2201.04127

  • Demo: https://youtu.be/GM-RoZEymmw


Visual Transformer

Backbone

MPViT : Multi-Path Vision Transformer for Dense Prediction

  • Paper: https://arxiv.org/abs/2112.11010

  • Code: https://github.com/youngwanLEE/MPViT

应用(Application)

Language-based Video Editing via Multi-Modal Multi-Level Transformer

  • Paper: https://arxiv.org/abs/2104.01122

  • Code: None

MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video

  • Paper: https://arxiv.org/abs/2203.00859

  • Code: None

Embracing Single Stride 3D Object Detector with Sparse Transformer

  • Paper: https://arxiv.org/abs/2112.06375

  • Code: https://github.com/TuSimple/SST

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

  • Paper: https://arxiv.org/abs/2203.02891

  • Code: https://github.com/xulianuwa/MCTformer

Spatio-temporal Relation Modeling for Few-shot Action Recognition

  • Paper: https://arxiv.org/abs/2112.05132

  • Code: https://github.com/Anirudh257/strm

Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction

  • Paper: https://arxiv.org/abs/2111.07910

  • Code: https://github.com/caiyuanhao1998/MST

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

  • Homepage: https://point-bert.ivg-research.xyz/

  • Paper: https://arxiv.org/abs/2111.14819

  • Code: https://github.com/lulutang0608/Point-BERT

GroupViT: Semantic Segmentation Emerges from Text Supervision

  • Homepage: https://jerryxu.net/GroupViT/

  • Paper: https://arxiv.org/abs/2202.11094

  • Demo: https://youtu.be/DtJsWIUTW-Y

Restormer: Efficient Transformer for High-Resolution Image Restoration

  • Paper: https://arxiv.org/abs/2111.09881

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

Splicing ViT Features for Semantic Appearance Transfer

  • Homepage: https://splice-vit.github.io/

  • Paper: https://arxiv.org/abs/2201.00424

  • Code: https://github.com/omerbt/Splice

Self-supervised Video Transformer

  • Homepage: https://kahnchana.github.io/svt/

  • Paper: https://arxiv.org/abs/2112.01514

  • Code: https://github.com/kahnchana/svt

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers

  • Paper: https://arxiv.org/abs/2203.02664

  • Code: https://github.com/rulixiang/afa

Accelerating DETR Convergence via Semantic-Aligned Matching

  • Paper: https://arxiv.org/abs/2203.06883

  • Code: https://github.com/ZhangGongjie/SAM-DETR

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

Style Transformer for Image Inversion and Editing

  • Paper: https://arxiv.org/abs/2203.07932

  • Code: https://github.com/sapphire497/style-transformer

MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer

  • Paper: https://arxiv.org/abs/2203.10981

  • Code: https://github.com/kuanchihhuang/MonoDTR

Mask Transfiner for High-Quality Instance Segmentation

  • Paper: https://arxiv.org/abs/2111.13673

  • Code: https://github.com/SysCV/transfiner


视觉和语言(Vision-Language)

Conditional Prompt Learning for Vision-Language Models

  • Paper: https://arxiv.org/abs/2203.05557

  • Code: https://github.com/KaiyangZhou/CoOp


自监督学习(Self-supervised Learning)

UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

  • Paper: https://arxiv.org/abs/2203.06965

  • Code: None

Crafting Better Contrastive Views for Siamese Representation Learning

HCSC: Hierarchical Contrastive Selective Coding


数据增强(Data Augmentation)

TeachAugment: Data Augmentation Optimization Using Teacher Knowledge

  • Paper: https://arxiv.org/abs/2202.12513

  • Code: https://github.com/DensoITLab/TeachAugment

AlignMix: Improving representation by interpolating aligned features

  • Paper: https://arxiv.org/abs/2103.15375

  • Code: None


目标检测(Object Detection)

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

Accelerating DETR Convergence via Semantic-Aligned Matching

  • Paper: https://arxiv.org/abs/2203.06883

  • Code: https://github.com/ZhangGongjie/SAM-DETR

Localization Distillation for Dense Object Detection

Focal and Global Knowledge Distillation for Detectors

A Dual Weighting Label Assignment Scheme for Object Detection

  • Paper: https://arxiv.org/abs/2203.09730

  • Code: https://github.com/strongwolf/DW


目标跟踪(Visual Tracking)

Correlation-Aware Deep Tracking

  • Paper: https://arxiv.org/abs/2203.01666

  • Code: None

TCTrack: Temporal Contexts for Aerial Tracking

  • Paper: https://arxiv.org/abs/2203.01885

  • Code: https://github.com/vision4robotics/TCTrack


语义分割(Semantic Segmentation)

弱监督语义分割

Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation

  • Paper: https://arxiv.org/abs/2203.00962

  • Code: https://github.com/zhaozhengChen/ReCAM

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

  • Paper: https://arxiv.org/abs/2203.02891

  • Code: https://github.com/xulianuwa/MCTformer

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers

  • Paper: https://arxiv.org/abs/2203.02664

  • Code: https://github.com/rulixiang/afa

半监督语义分割

ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

无监督语义分割

GroupViT: Semantic Segmentation Emerges from Text Supervision

  • Homepage: https://jerryxu.net/GroupViT/

  • Paper: https://arxiv.org/abs/2202.11094

  • Demo: https://youtu.be/DtJsWIUTW-Y


实例分割(Instance Segmentation)

E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

  • Paper: https://arxiv.org/abs/2203.04074

  • Code: https://github.com/zhang-tao-whu/e2ec

Mask Transfiner for High-Quality Instance Segmentation

  • Paper: https://arxiv.org/abs/2111.13673

  • Code: https://github.com/SysCV/transfiner

自监督实例分割

FreeSOLO: Learning to Segment Objects without Annotations

  • Paper: https://arxiv.org/abs/2202.12181

  • Code: None

视频实例分割

Efficient Video Instance Segmentation via Tracklet Query and Proposal

  • Homepage: https://jialianwu.com/projects/EfficientVIS.html

  • Paper: https://arxiv.org/abs/2203.01853

  • Demo: https://youtu.be/sSPMzgtMKCE


小样本分割(Few-Shot Segmentation)

Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

  • Paper: https://arxiv.org/abs/2203.07615

  • Code: https://github.com/chunbolang/BAM


视频理解(Video Understanding)

Self-supervised Video Transformer

  • Homepage: https://kahnchana.github.io/svt/

  • Paper: https://arxiv.org/abs/2112.01514

  • Code: https://github.com/kahnchana/svt

行为识别(Action Recognition)

Spatio-temporal Relation Modeling for Few-shot Action Recognition

  • Paper: https://arxiv.org/abs/2112.05132

  • Code: https://github.com/Anirudh257/strm

动作检测(Action Detection)

End-to-End Semi-Supervised Learning for Video Action Detection

  • Paper: https://arxiv.org/abs/2203.04251

  • Code: None


图像编辑(Image Editing)

Style Transformer for Image Inversion and Editing

  • Paper: https://arxiv.org/abs/2203.07932

  • Code: https://github.com/sapphire497/style-transformer

Blended Diffusion for Text-driven Editing of Natural Images

  • Paper: https://arxiv.org/abs/2111.14818

  • Code: https://github.com/omriav/blended-diffusion

SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing

  • Homepage: https://semanticstylegan.github.io/

  • Paper: https://arxiv.org/abs/2112.02236

  • Demo: https://semanticstylegan.github.io/videos/demo.mp4


Low-level Vision

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

  • Paper: https://arxiv.org/abs/2111.15362

  • Code: None

Restormer: Efficient Transformer for High-Resolution Image Restoration

  • Paper: https://arxiv.org/abs/2111.09881

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


超分辨率(Super-Resolution)

图像超分辨率(Image Super-Resolution)

Learning the Degradation Distribution for Blind Image Super-Resolution

  • Paper: https://arxiv.org/abs/2203.04962

  • Code: https://github.com/greatlog/UnpairedSR

视频超分辨率(Video Super-Resolution)

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment


3D点云(3D Point Cloud)

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

  • Homepage: https://point-bert.ivg-research.xyz/

  • Paper: https://arxiv.org/abs/2111.14819

  • Code: https://github.com/lulutang0608/Point-BERT

A Unified Query-based Paradigm for Point Cloud Understanding

  • Paper: https://arxiv.org/abs/2203.01252

  • Code: None

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding

  • Paper: https://arxiv.org/abs/2203.00680

  • Code: https://github.com/MohamedAfham/CrossPoint

PointCLIP: Point Cloud Understanding by CLIP

  • Paper: https://arxiv.org/abs/2112.02413

  • Code: https://github.com/ZrrSkywalker/PointCLIP


3D目标检测(3D Object Detection)

Embracing Single Stride 3D Object Detector with Sparse Transformer

  • Paper: https://arxiv.org/abs/2112.06375

  • Code: https://github.com/TuSimple/SST

Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes

  • Paper: https://arxiv.org/abs/2011.12001

  • Code: https://github.com/qq456cvb/CanonicalVoting

MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer

  • Paper: https://arxiv.org/abs/2203.10981

  • Code: https://github.com/kuanchihhuang/MonoDTR


3D语义分割(3D Semantic Segmentation)

Scribble-Supervised LiDAR Semantic Segmentation

  • Paper: https://arxiv.org/abs/2203.08537

  • Dataset: https://github.com/ouenal/scribblekitti


3D目标跟踪(3D Object Tracking)

Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds

  • Paper: https://arxiv.org/abs/2203.01730

  • Code: https://github.com/Ghostish/Open3DSOT


3D人体姿态估计(3D Human Pose Estimation)

MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation

  • Paper: https://arxiv.org/abs/2111.12707

  • Code: https://github.com/Vegetebird/MHFormer

  • 中文解读: https://zhuanlan.zhihu.com/p/439459426

MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video

  • Paper: https://arxiv.org/abs/2203.00859

  • Code: None


3D语义场景补全(3D Semantic Scene Completion)

MonoScene: Monocular 3D Semantic Scene Completion

  • Paper: https://arxiv.org/abs/2112.00726

  • Code: https://github.com/cv-rits/MonoScene


3D重建(3D Reconstruction)

BANMo: Building Animatable 3D Neural Models from Many Casual Videos


伪装物体检测(Camouflaged Object Detection)

Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection

  • Paper: https://arxiv.org/abs/2203.02688

  • Code: https://github.com/lartpang/ZoomNet


深度估计(Depth Estimation)

单目深度估计

NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

  • Paper: https://arxiv.org/abs/2203.01502

  • Code: None

OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion

  • Paper: https://arxiv.org/abs/2203.00838

  • Code: None

Toward Practical Self-Supervised Monocular Indoor Depth Estimation

  • Paper: https://arxiv.org/abs/2112.02306

  • Code: None


立体匹配(Stereo Matching)

ACVNet: Attention Concatenation Volume for Accurate and Efficient Stereo Matching

  • Paper: https://arxiv.org/abs/2203.02146

  • Code: https://github.com/gangweiX/ACVNet


车道线检测(Lane Detection)

Rethinking Efficient Lane Detection via Curve Modeling

  • Paper: https://arxiv.org/abs/2203.02431

  • Code: https://github.com/voldemortX/pytorch-auto-drive

  • Demo：https://user-images.githubusercontent.com/32259501/148680744-a18793cd-f437-461f-8c3a-b909c9931709.mp4


图像修复(Image Inpainting)

Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding

  • Paper: https://arxiv.org/abs/2203.00867

  • Code: https://github.com/DQiaole/ZITS_inpainting


人群计数(Crowd Counting)

Leveraging Self-Supervision for Cross-Domain Crowd Counting

  • Paper: https://arxiv.org/abs/2103.16291

  • Code: None


医学图像(Medical Image)

BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation

  • Paper: https://arxiv.org/abs/2203.02533

  • Code: None


场景图生成(Scene Graph Generation)

SGTR: End-to-end Scene Graph Generation with Transformer

  • Paper: https://arxiv.org/abs/2112.12970

  • Code: None


风格迁移(Style Transfer)

StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions

  • Homepage: https://lukashoel.github.io/stylemesh/

  • Paper: https://arxiv.org/abs/2112.01530

  • Code: https://github.com/lukasHoel/stylemesh

  • Demo：https://www.youtube.com/watch?v=ZqgiTLcNcks


弱监督物体检测(Weakly Supervised Object Localization)

Weakly Supervised Object Localization as Domain Adaption

  • Paper: https://arxiv.org/abs/2203.01714

  • Code: https://github.com/zh460045050/DA-WSOL_CVPR2022


高光谱图像重建(Hyperspectral Image Reconstruction)

Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction

  • Paper: https://arxiv.org/abs/2111.07910

  • Code: https://github.com/caiyuanhao1998/MST


水印(Watermarking)

Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings

  • Paper: https://arxiv.org/abs/2104.13450

  • Code: None


数据集(Datasets)

It's About Time: Analog Clock Reading in the Wild

  • Homepage: https://charigyang.github.io/abouttime/

  • Paper: https://arxiv.org/abs/2111.09162

  • Code: https://github.com/charigyang/itsabouttime

  • Demo: https://youtu.be/cbiMACA6dRc

Toward Practical Self-Supervised Monocular Indoor Depth Estimation

  • Paper: https://arxiv.org/abs/2112.02306

  • Code: None

Kubric: A scalable dataset generator

  • Paper: https://arxiv.org/abs/2203.03570

  • Code: https://github.com/google-research/kubric

Scribble-Supervised LiDAR Semantic Segmentation

  • Paper: https://arxiv.org/abs/2203.08537

  • Dataset: https://github.com/ouenal/scribblekitti


新任务(New Task)

Language-based Video Editing via Multi-Modal Multi-Level Transformer

  • Paper: https://arxiv.org/abs/2104.01122

  • Code: None

It's About Time: Analog Clock Reading in the Wild

  • Homepage: https://charigyang.github.io/abouttime/

  • Paper: https://arxiv.org/abs/2111.09162

  • Code: https://github.com/charigyang/itsabouttime

  • Demo: https://youtu.be/cbiMACA6dRc

Splicing ViT Features for Semantic Appearance Transfer

  • Homepage: https://splice-vit.github.io/

  • Paper: https://arxiv.org/abs/2201.00424

  • Code: https://github.com/omerbt/Splice


其他(Others)

Kubric: A scalable dataset generator

  • Paper: https://arxiv.org/abs/2203.03570

  • Code: https://github.com/google-research/kubric



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【Keras】完整实现‘交通标志’分类、‘票据’分类两个项目，让你掌握深度学习图像分类


VGG16迁移学习，实现医学图像识别分类工程项目


特征工程(一)


特征工程(二) :文本数据的展开、过滤和分块


特征工程(三):特征缩放,从词袋到 TF-IDF


特征工程(四): 类别特征


特征工程(五): PCA 降维


特征工程(六): 非线性特征提取和模型堆叠


特征工程(七)：图像特征提取和深度学习


如何利用全新的决策树集成级联结构gcForest做特征工程并打分？


Machine Learning Yearning 中文翻译稿


蚂蚁金服2018秋招-算法工程师（共四面）通过


全球AI挑战-场景分类的比赛源码(多模型融合)


斯坦福CS230官方指南：CNN、RNN及使用技巧速查（打印收藏）


python+flask搭建CNN在线识别手写中文网站


中科院Kaggle全球文本匹配竞赛华人第1名团队-深度学习与特征工程



不断更新资源

深度学习、机器学习、数据分析、python

 搜索公众号添加： datayx  


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