目录
- AutoFormer: Searching Transformers for Visual Recognition新建 AutoFormer: Searching Transformers for Visual Recognition4年前
- Bias Loss for Mobile Neural Networks新建 Bias Loss for Mobile Neural Networks4年前
- Conformer: Local Features Coupling Global Representations for Visual Recognition新建 Conformer: Local Features Coupling Global Representations for Visual Recognition4年前
- MicroNet: Improving Image Recognition with Extremely Low FLOPs新建 MicroNet: Improving Image Recognition with Extremely Low FLOPs4年前
- Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without ConvolutionsPVT24年前
- Rethinking Spatial Dimensions of Vision Transformers新建 Rethinking Spatial Dimensions of Vision Transformers4年前
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows新建 Swin Transformer: Hierarchical Vision Transformer using Shifted Windows4年前
- Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet新建 Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet4年前
- Vision Transformer with Progressive Sampling新建 Vision Transformer with Progressive Sampling4年前
- Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition新建 Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition4年前
- LICENSEadd LICENSE.4年前
- README.en.mdInitial commit4年前
- README.mdupdate README.md.4年前
- Backbone
- Transformer
- 涨点神器
- GAN
- NAS
- NeRF
- Loss
- Zero-Shot Learning
- Few-Shot Learning
- 长尾(Long-tailed)
- Vision and Language
- 无监督/自监督(Self-Supervised)
- Multi-Label Image Recognition(多标签图像识别)
- 2D目标检测(Object Detection)
- 语义分割(Semantic Segmentation)
- 实例分割(Instance Segmentation)
- 医学图像分割(Medical Image Segmentation)
- 视频目标分割(Video Object Segmentation)
- Few-shot Segmentation
- 人体运动分割(Human Motion Segmentation)
- 目标跟踪(Object Tracking)
- 3D Point Cloud
- 3D Object Detection(3D目标检测)
- 3D Semantic Segmenation(3D语义分割)
- 3D Instance Segmentation(3D实例分割)
- 3D Multi-Object Tracking(3D多目标跟踪)
- Point Cloud Denoising(点云去噪)
- Point Cloud Registration(点云配准)
- Point Cloud Completion(点云补全)
- 雷达语义分割(Radar Semantic Segmentation)
- 图像恢复(Image Restoration)
- 超分辨率(Super-Resolution)
- 去噪(Denoising)
- 医学图像去噪(Medical Image Denoising)
- 去模糊(Deblurring)
- 阴影去除(Shadow Removal)
- 视频插帧(Video Frame Interpolation)
- 视频修复/补全(Video Inpainting)
- 行人重识别(Person Re-identification)
- 行人搜索(Person Search)
- 2D/3D人体姿态估计(2D/3D Human Pose Estimation)
- 6D位姿估计(6D Object Pose Estimation)
- 3D人头重建(3D Head Reconstruction)
- 人脸识别(Face Recognition)
- 人脸表情识别(Facial Expression Recognition)
- 行为识别(Action Recognition)
- 时序动作定位(Temporal Action Localization)
- 动作检测(Action Detection)
- 群体活动识别(Group Activity Recognition)
- 手语识别(Sign Language Recognition)
- 文本检测(Text Detection)
- 文本识别(Text Recognition)
- 文本替换(Text Repalcement)
- 视觉问答(Visual Question Answering, VQA)
- 对抗攻击(Adversarial Attack)
- 深度估计(Depth Estimation)
- 视线估计(Gaze Estimation)
- 人群计数(Crowd Counting)
- 车道线检测(Lane Detection)
- 轨迹预测(Trajectory Prediction)
- 异常检测(Anomaly Detection)
- 场景图生成(Scene Graph Generation)
- 图像编辑(Image Editing)
- 图像合成(Image Synthesis)
- 图像检索(Image Retrieval)
- 三维重建(3D Reconstruction)
- 视频稳像(Video Stabilization)
- 细粒度识别(Fine-Grained Recognition)
- 风格迁移(Style Transfer)
- 神经绘画(Neural Painting)
- 特征匹配(Feature Matching)
- 语义对应(Semantic Correspondence)
- 边缘检测(Edge Detection)
- 相机标定(Camera Calibration)
- 图像质量评估(Image Quality Assessment)
- 度量学习(Metric Learning)
- Unsupervised Domain Adaptation
- Video Rescaling
- Hand-Object Interaction
- Vision-and-Language Navigation
- 数据集(Datasets)
- 其他(Others)
- Backbone
- Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
- Paper(Oral): https://arxiv.org/abs/2102.12122
- Code: https://github.com/whai362/PVT
- AutoFormer: Searching Transformers for Visual Recognition
- Paper: https://arxiv.org/abs/2107.00651
- Code: https://github.com/microsoft/AutoML
- Bias Loss for Mobile Neural Networks
- Paper: https://arxiv.org/abs/2107.11170
- Code: None
- Vision Transformer with Progressive Sampling
- Paper: https://arxiv.org/abs/2108.01684
- Code: https://github.com/yuexy/PS-ViT
- Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
- Paper: https://arxiv.org/abs/2101.11986
- Code: https://github.com/yitu-opensource/T2T-ViT
- Rethinking Spatial Dimensions of Vision Transformers
- Paper: https://arxiv.org/abs/2103.16302
- Code: https://github.com/naver-ai/pit
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
- Paper: https://arxiv.org/abs/2103.14030
- Code: https://github.com/microsoft/Swin-Transformer
- Conformer: Local Features Coupling Global Representations for Visual Recognition
- Paper: https://arxiv.org/abs/2105.03889
- Code: https://github.com/pengzhiliang/Conformer
- MicroNet: Improving Image Recognition with Extremely Low FLOPs
- Paper: https://arxiv.org/abs/2108.05894
- Code: https://github.com/liyunsheng13/micronet
- Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
- Paper: https://arxiv.org/abs/2102.01063
- Code: https://github.com/idstcv/ZenNAS
- Visual Transformer
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
- Paper: https://arxiv.org/abs/2103.14030
- Code: https://github.com/microsoft/Swin-Transformer
- An Empirical Study of Training Self-Supervised Vision Transformers
- Paper(Oral): https://arxiv.org/abs/2104.02057
- MoCo v3 Code: None
- Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
- Paper(Oral): https://arxiv.org/abs/2102.12122
- Code: https://github.com/whai362/PVT
- Group-Free 3D Object Detection via Transformers
- Paper: https://arxiv.org/abs/2104.00678
- Code: None
- Spatial-Temporal Transformer for Dynamic Scene Graph Generation
- Paper: https://arxiv.org/abs/2107.12309
- Code: None
- Rethinking and Improving Relative Position Encoding for Vision Transformer
- Paper: https://arxiv.org/abs/2107.14222
- Code: https://github.com/microsoft/AutoML/tree/main/iRPE
- Emerging Properties in Self-Supervised Vision Transformers
- Paper: https://arxiv.org/abs/2104.14294
- Code: https://github.com/facebookresearch/dino
- Learning Spatio-Temporal Transformer for Visual Tracking
- Paper: https://arxiv.org/abs/2103.17154
- Code: https://github.com/researchmm/Stark
- Fast Convergence of DETR with Spatially Modulated Co-Attention
- Paper: https://arxiv.org/abs/2101.07448
- Code: https://github.com/abc403/SMCA-replication
- Vision Transformer with Progressive Sampling
- Paper: https://arxiv.org/abs/2108.01684
- Code: https://github.com/yuexy/PS-ViT
- Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
- Paper: https://arxiv.org/abs/2101.11986
- Code: https://github.com/yitu-opensource/T2T-ViT
- Rethinking Spatial Dimensions of Vision Transformers
- Paper: https://arxiv.org/abs/2103.16302
- Code: https://github.com/naver-ai/pit
- The Right to Talk: An Audio-Visual Transformer Approach
- Paper: https://arxiv.org/abs/2108.03256
- Code: None
- Joint Inductive and Transductive Learning for Video Object Segmentation
- Paper: https://arxiv.org/abs/2108.03679
- Code: https://github.com/maoyunyao/JOINT
- Conformer: Local Features Coupling Global Representations for Visual Recognition
- Paper: https://arxiv.org/abs/2105.03889
- Code: https://github.com/pengzhiliang/Conformer
- Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
- Paper: https://arxiv.org/abs/2108.03032
- Code: https://github.com/zhiheLu/CWT-for-FSS
- Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
- Paper: https://arxiv.org/abs/2108.03798
- Code: https://github.com/wzmsltw/PaintTransformer
- Conditional DETR for Fast Training Convergence
- Paper: https://arxiv.org/abs/2108.06152
- Code: https://github.com/Atten4Vis/ConditionalDETR
- MUSIQ: Multi-scale Image Quality Transformer
- Paper: https://arxiv.org/abs/2108.05997
- Code: https://github.com/google-research/google-research/tree/master/musiq
- SOTR: Segmenting Objects with Transformers
- Paper: https://arxiv.org/abs/2108.06747
- Code: https://github.com/easton-cau/SOTR
- PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
- Paper(Oral): https://arxiv.org/abs/2108.08839
- Code: https://github.com/yuxumin/PoinTr
- SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
- Paper: https://arxiv.org/abs/2108.04444
- Code: https://github.com/AllenXiangX/SnowflakeNet
- Improving 3D Object Detection with Channel-wise Transformer
- Paper: https://arxiv.org/abs/2108.10723
- Code: https://github.com/hlsheng1/CT3D
- TransFER: Learning Relation-aware Facial Expression Representations with Transformers
- Paper: https://arxiv.org/abs/2108.11116
- Code: None
- GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
- Paper: https://arxiv.org/abs/2108.12630
- Code: https://github.com/xueyee/GroupFormer
- Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
- Paper: https://arxiv.org/abs/2109.00512
- Code: https://github.com/facebookresearch/co3d
- Dataset: https://github.com/facebookresearch/co3d
- Voxel Transformer for 3D Object Detection
- Paper: https://arxiv.org/abs/2109.02497
- Code: None
- 3D Human Texture Estimation from a Single Image with Transformers
- Homepage: https://www.mmlab-ntu.com/project/texformer/
- Paper(Oral): https://arxiv.org/abs/2109.02563
- Code: None
- FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
- Paper: https://arxiv.org/abs/2109.02974
- Code: https://github.com/ruiliu-ai/FuseFormer
- CTRL-C: Camera calibration TRansformer with Line-Classification
- Paper: https://arxiv.org/abs/2109.02259
- Code: https://github.com/jwlee-vcl/CTRL-C
- An End-to-End Transformer Model for 3D Object Detection
- Homepage: https://facebookresearch.github.io/3detr/
- Paper: https://arxiv.org/abs/2109.08141
- Code: https://github.com/facebookresearch/3detr
- Eformer: Edge Enhancement based Transformer for Medical Image Denoising
- Paper: https://arxiv.org/abs/2109.08044
- Code: None
- PnP-DETR: Towards Efficient Visual Analysis with Transformers
- Paper: https://arxiv.org/abs/2109.07036
- Code: https://github.com/twangnh/pnp-detr
- Transformer-based Dual Relation Graph for Multi-label Image Recognition
- Paper: https://arxiv.org/abs/2110.04722
- Code: None
- 涨点神器
- FaPN: Feature-aligned Pyramid Network for Dense Image Prediction
- Paper: https://github.com/EMI-Group/FaPN
- Code: https://arxiv.org/abs/2108.07058
- Unifying Nonlocal Blocks for Neural Networks
- Paper: https://arxiv.org/abs/2108.02451
- Code: https://github.com/zh460045050/SNL_ICCV2021
- Towards Learning Spatially Discriminative Feature Representations
- Paper: https://arxiv.org/abs/2109.01359
- Code: None
- GAN
- Labels4Free: Unsupervised Segmentation using StyleGAN
- Homepage: https://rameenabdal.github.io/Labels4Free/
- Paper: https://arxiv.org/abs/2103.14968
- GNeRF: GAN-based Neural Radiance Field without Posed Camera
- Paper(Oral): https://arxiv.org/abs/2103.15606
- Code: https://github.com/MQ66/gnerf
- EigenGAN: Layer-Wise Eigen-Learning for GANs
- Paper: https://arxiv.org/abs/2104.12476
- Code: https://github.com/LynnHo/EigenGAN-Tensorflow
- From Continuity to Editability: Inverting GANs with Consecutive Images
- Paper: https://arxiv.org/abs/2107.13812
- Code: https://github.com/Qingyang-Xu/InvertingGANs_with_ConsecutiveImgs
- Sketch Your Own GAN
- Homepage: https://peterwang512.github.io/GANSketching/
- Paper: https://arxiv.org/abs/2108.02774
- 代码: https://github.com/peterwang512/GANSketching
- Manifold Matching via Deep Metric Learning for Generative Modeling
- Paper: https://arxiv.org/abs/2106.10777
- Code: https://github.com/dzld00/pytorch-manifold-matching
- Dual Projection Generative Adversarial Networks for Conditional Image Generation
- Paper: https://arxiv.org/abs/2108.09016
- Code: None
- GAN Inversion for Out-of-Range Images with Geometric Transformations
- Paper: https://arxiv.org/abs/2108.08998
- Code: None
- ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement
- Homepage: https://yuval-alaluf.github.io/restyle-encoder/
- Paper: https://arxiv.org/abs/2104.02699
- Code: https://github.com/yuval-alaluf/restyle-encoder
- StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
- Paper(Oral): https://arxiv.org/abs/2103.17249
- Code: https://github.com/orpatashnik/StyleCLIP
- Image Synthesis via Semantic Composition
- Homepage: https://shepnerd.github.io/scg/
- Paper: https://arxiv.org/abs/2109.07053
- Code: https://github.com/dvlab-research/SCGAN
- NAS
- AutoFormer: Searching Transformers for Visual Recognition
- Paper: https://arxiv.org/abs/2107.00651
- Code: https://github.com/microsoft/AutoML
- BN-NAS: Neural Architecture Search with Batch Normalization
- Paper: https://arxiv.org/abs/2108.07375
- Code: https://github.com/bychen515/BNNAS
- Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
- Paper: https://arxiv.org/abs/2102.01063
- Code: https://github.com/idstcv/ZenNAS
- NeRF
- GNeRF: GAN-based Neural Radiance Field without Posed Camera
- Paper(Oral): https://arxiv.org/abs/2103.15606
- Code: https://github.com/MQ66/gnerf
- KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
- Paper: https://arxiv.org/abs/2103.13744
- Code: https://github.com/creiser/kilonerf
- In-Place Scene Labelling and Understanding with Implicit Scene Representation
- Homepage: https://shuaifengzhi.com/Semantic-NeRF/
- Paper(Oral): https://arxiv.org/abs/2103.15875
- Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis
- Homepage: https://ajayj.com/dietnerf
- Paper(DietNeRF): https://arxiv.org/abs/2104.00677
- BARF: Bundle-Adjusting Neural Radiance Fields
- Homepage: https://chenhsuanlin.bitbucket.io/bundle-adjusting-NeRF/
- Paper(Oral): https://arxiv.org/abs/2104.06405
- Code: https://github.com/chenhsuanlin/bundle-adjusting-NeRF
- Self-Calibrating Neural Radiance Fields
- Paper: https://arxiv.org/abs/2108.13826
- Code: https://github.com/POSTECH-CVLab/SCNeRF
- Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
- Paper: https://arxiv.org/abs/2109.00512
- Code: https://github.com/facebookresearch/co3d
- Dataset: https://github.com/facebookresearch/co3d
- Neural Articulated Radiance Field
- Paper: https://arxiv.org/abs/2104.03110
- Code: https://github.com/nogu-atsu/NARF
- NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo
- Paper(Oral): https://arxiv.org/abs/2109.01129
- Code: https://github.com/weiyithu/NerfingMVS
- SNARF: Differentiable Forward Skinning for Animating Non-rigid Neural Implicit Shapes
- Homepage: https://xuchen-ethz.github.io/snarf
- Paper: https://arxiv.org/abs/2104.03953
- Code: https://github.com/xuchen-ethz/snarf
- CodeNeRF: Disentangled Neural Radiance Fields for Object Categories
- Paper: https://arxiv.org/abs/2109.01750
- Code: https://github.com/wayne1123/code-nerf
- Loss
- Rank & Sort Loss for Object Detection and Instance Segmentation
- Paper(Oral): https://arxiv.org/abs/2107.11669
- Code: https://github.com/kemaloksuz/RankSortLoss
- Bias Loss for Mobile Neural Networks
- Paper: https://arxiv.org/abs/2107.11170
- Code: None
- A Robust Loss for Point Cloud Registration
- Paper: https://arxiv.org/abs/2108.11682
- Code: None
- Reconcile Prediction Consistency for Balanced Object Detection
- Paper: https://arxiv.org/abs/2108.10809
- Code: None
- Influence-Balanced Loss for Imbalanced Visual Classification
- Paper: https://arxiv.org/abs/2110.02444
- Code: https://github.com/pseulki/IB-Loss
- Zero-Shot Learning
- FREE: Feature Refinement for Generalized Zero-Shot Learning
- Paper: https://arxiv.org/abs/2107.13807
- Code: https://github.com/shiming-chen/FREE
- Discriminative Region-based Multi-Label Zero-Shot Learning
- Paper: https://arxiv.org/abs/2108.09301
- Code: https://arxiv.org/abs/2108.09301
- Few-Shot Learning
- Relational Embedding for Few-Shot Classification
- Paper: https://arxiv.org/abs/2108.0966
- Code: https://github.com/dahyun-kang/renet
- Few-Shot and Continual Learning with Attentive Independent Mechanisms
- Paper: https://arxiv.org/abs/2107.14053
- Code: https://github.com/huang50213/AIM-Fewshot-Continual
- 长尾(Long-tailed)
- Parametric Contrastive Learning
- Paper: https://arxiv.org/abs/2107.12028
- Code: https://github.com/jiequancui/Parametric-Contrastive-Learning
- Influence-Balanced Loss for Imbalanced Visual Classification
- Paper: https://arxiv.org/abs/2110.02444
- Code: https://github.com/pseulki/IB-Loss
- Vision and Language
- VLGrammar: Grounded Grammar Induction of Vision and Language
- Paper: https://arxiv.org/abs/2103.12975
- Code: https://github.com/evelinehong/VLGrammar
- 无监督/自监督(Un/Self-Supervised)
- An Empirical Study of Training Self-Supervised Vision Transformers
- Paper(Oral): https://arxiv.org/abs/2104.02057
- MoCo v3 Code: None
- DetCo: Unsupervised Contrastive Learning for Object Detection
- Paper: https://arxiv.org/abs/2102.04803
- Code: https://github.com/xieenze/DetCo
- Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
- Paper: https://arxiv.org/abs/2108.02183
- Code: None
- Improving Contrastive Learning by Visualizing Feature Transformation
- Paper(Oral): https://arxiv.org/abs/2108.02982
- Code: https://github.com/DTennant/CL-Visualizing-Feature-Transformation
- Self-Supervised Visual Representations Learning by Contrastive Mask Prediction
- Paper: https://arxiv.org/abs/2108.08012
- Code: None
- Temporal Knowledge Consistency for Unsupervised Visual Representation Learning
- Paper: https://arxiv.org/abs/2108.10668
- Code: None
- MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving
- Paper: https://arxiv.org/abs/2108.12178
- Code: https://github.com/KaiChen1998/MultiSiam
- Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
- Homepage: https://siyuanhuang.com/STRL/
- Paper: https://arxiv.org/abs/2109.00179
- Code: https://github.com/yichen928/STRL
- Self-supervised Product Quantization for Deep Unsupervised Image Retrieval
- Paper: https://arxiv.org/abs/2109.02244
- Code: https://github.com/youngkyunJang/SPQ
- Multi-Label Image Recognition(多标签图像识别)
- Residual Attention: A Simple but Effective Method for Multi-Label Recognition
- Paper: https://arxiv.org/abs/2108.02456
- Code: https://github.com/Kevinz-code/CSRA
- 2D目标检测(Object Detection)
- DetCo: Unsupervised Contrastive Learning for Object Detection
- Paper: https://arxiv.org/abs/2102.04803
- Code: https://github.com/xieenze/DetCo
- Detecting Invisible People
- Homepage: http://www.cs.cmu.edu/~tkhurana/invisible.htm
- Code: https://arxiv.org/abs/2012.08419
- Active Learning for Deep Object Detection via Probabilistic Modeling
- Paper: https://arxiv.org/abs/2103.16130
- Code: None
- Conditional Variational Capsule Network for Open Set Recognition
- Paper: https://arxiv.org/abs/2104.09159
- Code: https://github.com/guglielmocamporese/cvaecaposr
- MDETR : Modulated Detection for End-to-End Multi-Modal Understanding
- Homepage: https://ashkamath.github.io/mdetr_page/
- Paper(Oral): https://arxiv.org/abs/2104.12763
- Code: https://github.com/ashkamath/mdetr
- Rank & Sort Loss for Object Detection and Instance Segmentation
- Paper(Oral): https://arxiv.org/abs/2107.11669
- Code: https://github.com/kemaloksuz/RankSortLoss
- SimROD: A Simple Adaptation Method for Robust Object Detection
- Paper(Oral): https://arxiv.org/abs/2107.13389
- Code: None
- GraphFPN: Graph Feature Pyramid Network for Object Detection
- Paper: https://arxiv.org/abs/2108.00580
- Code: None
- Fast Convergence of DETR with Spatially Modulated Co-Attention
- Paper: https://arxiv.org/abs/2101.07448
- Code: https://github.com/abc403/SMCA-replication
- Conditional DETR for Fast Training Convergence
- Paper: https://arxiv.org/abs/2108.06152
- Code: https://github.com/Atten4Vis/ConditionalDETR
- TOOD: Task-aligned One-stage Object Detection
- Paper(Oral): https://arxiv.org/abs/2108.07755
- Code: https://github.com/fcjian/TOOD
- Reconcile Prediction Consistency for Balanced Object Detection
- Paper: https://arxiv.org/abs/2108.10809
- Code: None
- Mutual Supervision for Dense Object Detection
- Paper: https://arxiv.org/abs/2109.05986
- Code: https://github.com/MCG-NJU/MuSu-Detection
- PnP-DETR: Towards Efficient Visual Analysis with Transformers
- Paper: https://arxiv.org/abs/2109.07036
- Code: https://github.com/twangnh/pnp-detr
- Deep Structured Instance Graph for Distilling Object Detectors
- Paper: https://arxiv.org/abs/2109.12862
- Code: https://github.com/dvlab-research/Dsig
- 半监督目标检测
- End-to-End Semi-Supervised Object Detection with Soft Teacher
- Paper: https://arxiv.org/abs/2106.09018
- Code: None
- 旋转目标检测
- Oriented R-CNN for Object Detection
- Paper: https://arxiv.org/abs/2108.05699
- Code: https://github.com/jbwang1997/OBBDetection
- Few-Shot目标检测
- DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
- Paper: https://arxiv.org/abs/2108.09017
- Code: https://github.com/er-muyue/DeFRCN
- 语义分割(Semantic Segmentation)
- Personalized Image Semantic Segmentation
- Paper: https://arxiv.org/abs/2107.13978
- Code: https://github.com/zhangyuygss/PIS
- Dataset: https://github.com/zhangyuygss/PIS
- Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
- Paper(Oral): https://arxiv.org/abs/2107.11264
- Code: None
- Enhanced Boundary Learning for Glass-like Object Segmentation
- Paper: https://arxiv.org/abs/2103.15734
- Code: https://github.com/hehao13/EBLNet
- Self-Regulation for Semantic Segmentation
- Paper: https://arxiv.org/abs/2108.09702
- Code: https://github.com/dongzhang89/SR-SS
- Mining Contextual Information Beyond Image for Semantic Segmentation
- Paper: https://arxiv.org/abs/2108.11819
- Code: https://github.com/CharlesPikachu/mcibi
- Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
- Paper: https://arxiv.org/abs/2107.11264
- Code: https://github.com/shjung13/Standardized-max-logits
- ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation
- Paper: https://arxiv.org/abs/2108.12382
- Code: https://github.com/SegmentationBLWX/sssegmentation
- Scaling up instance annotation via label propagation
- Homepage: http://scaling-anno.csail.mit.edu/
- Paper: https://arxiv.org/abs/2110.02277
- Code: None
- 无监督域自适应语义分割(Unsupervised Domain Ddaption Semantic Segmentation)
- Multi-Anchor Active Domain Adaptation for Semantic Segmentation
- Paper(Oral): https://arxiv.org/abs/2108.08012
- Code: https://github.com/munanning/MADA
- 论文下载链接：https://arxiv.org/abs/2108.08012
- Few-Shot语义分割
- Learning Meta-class Memory for Few-Shot Semantic Segmentation
- Paper: https://arxiv.org/abs/2108.02958'
- Code: None
- Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
- Paper: https://arxiv.org/abs/2108.03032
- Code: https://github.com/zhiheLu/CWT-for-FSS
- 半监督语义分割(Semi-supervised Semantic Segmentation)
- Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation
- Paper: https://arxiv.org/abs/2107.11787
- Code: None
- Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation
- Paper(Oral): https://arxiv.org/abs/2107.11279
- Code: https://github.com/CVMI-Lab/DARS
- Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
- Paper: https://arxiv.org/abs/2108.09025
- Code: None
- 弱监督语义分割(Weakly Supervised Semantic Segmentation)
- Complementary Patch for Weakly Supervised Semantic Segmentation
- Paper: https://arxiv.org/abs/2108.03852
- Code: None
- 无监督分割(Unsupervised Segmentation)
- Labels4Free: Unsupervised Segmentation using StyleGAN
- Homepage: https://rameenabdal.github.io/Labels4Free/
- Paper: https://arxiv.org/abs/2103.14968
- 实例分割(Instance Segmentation)
- Instances as Queries
- Paper: https://arxiv.org/abs/2105.01928
- Code: https://github.com/hustvl/QueryInst
- Crossover Learning for Fast Online Video Instance Segmentation
- Paper: https://arxiv.org/abs/2104.05970
- Code: https://github.com/hustvl/CrossVIS
- Rank & Sort Loss for Object Detection and Instance Segmentation
- Paper(Oral): https://arxiv.org/abs/2107.11669
- Code: https://github.com/kemaloksuz/RankSortLoss
- SOTR: Segmenting Objects with Transformers
- Paper: https://arxiv.org/abs/2108.06747
- Code: https://github.com/easton-cau/SOTR
- Scaling up instance annotation via label propagation
- Homepage: http://scaling-anno.csail.mit.edu/
- Paper: https://arxiv.org/abs/2110.02277
- Code: None
- 医学图像分割(Medical Image Segmentation)
- Recurrent Mask Refinement for Few-Shot Medical Image Segmentation
- Paper: https://arxiv.org/abs/2108.00622
- Code: https://github.com/uci-cbcl/RP-Net
- 视频目标分割(Video Object Segmentation)
- Hierarchical Memory Matching Network for Video Object Segmentation
- Paper: https://arxiv.org/abs/2109.11404
- Code: https://github.com/Hongje/HMMN
- Full-Duplex Strategy for Video Object Segmentation
- Homepage: http://dpfan.net/FSNet/
- Paper: https://arxiv.org/abs/2108.03151
- Code: https://github.com/GewelsJI/FSNet
- Joint Inductive and Transductive Learning for Video Object Segmentation
- Paper: https://arxiv.org/abs/2108.03679
- Code: https://github.com/maoyunyao/JOINT
- Few-shot Segmentation
- Mining Latent Classes for Few-shot Segmentation
- Paper(Oral): https://arxiv.org/abs/2103.15402
- Code: https://github.com/LiheYoung/MiningFSS
- 人体运动分割(Human Motion Segmentation)
- Graph Constrained Data Representation Learning for Human Motion Segmentation
- Paper: https://arxiv.org/abs/2107.13362
- Code: None
- 目标跟踪(Object Tracking)
- Learning to Track Objects from Unlabeled Videos
- Paper: https://arxiv.org/abs/2108.12711
- Code: https://github.com/VISION-SJTU/USOT
- Learning Spatio-Temporal Transformer for Visual Tracking
- Paper: https://arxiv.org/abs/2103.17154
- Code: https://github.com/researchmm/Stark
- Learning to Adversarially Blur Visual Object Tracking
- Paper: https://arxiv.org/abs/2107.12085
- Code: https://github.com/tsingqguo/ABA
- HiFT: Hierarchical Feature Transformer for Aerial Tracking
- Paper: https://arxiv.org/abs/2108.00202
- Code: https://github.com/vision4robotics/HiFT
- Learn to Match: Automatic Matching Network Design for Visual Tracking
- Paper: https://arxiv.org/abs/2108.00803
- Code: https://github.com/JudasDie/SOTS
- Saliency-Associated Object Tracking
- Paper: https://arxiv.org/abs/2108.03637
- Code: https://github.com/ZikunZhou/SAOT.git
- RGBD 目标跟踪
- DepthTrack: Unveiling the Power of RGBD Tracking
- Paper: https://arxiv.org/abs/2108.13962
- Code: https://github.com/xiaozai/DeT
- Dataset: https://github.com/xiaozai/DeT
- 3D Point Cloud
- Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
- Homepage: https://siyuanhuang.com/STRL/
- Paper: https://arxiv.org/abs/2109.00179
- Code: https://github.com/yichen928/STRL
- Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion
- Homepage: https://hansen7.github.io/OcCo/
- Paper: https://arxiv.org/abs/2010.01089
- Code: https://github.com/hansen7/OcCo
- DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation
- Paper: https://arxiv.org/abs/2108.04023
- Code: None
- Adaptive Graph Convolution for Point Cloud Analysis
- Paper: https://arxiv.org/abs/2108.08035
- Code: https://github.com/hrzhou2/AdaptConv-master
- Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion
- Paper: https://arxiv.org/abs/2010.01089
- Code: https://github.com/hansen7/OcCo
- 3D Object Detection(3D目标检测)
- Group-Free 3D Object Detection via Transformers
- Paper: https://arxiv.org/abs/2104.00678
- Code: None
- Improving 3D Object Detection with Channel-wise Transformer
- Paper: https://arxiv.org/abs/2108.10723
- Code: https://github.com/hlsheng1/CT3D
- AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection
- Paper: https://arxiv.org/abs/2108.11127
- Code: https://github.com/zongdai/AutoShape
- 4D-Net for Learned Multi-Modal Alignment
- Paper: https://arxiv.org/abs/2109.01066
- Code: None
- Voxel Transformer for 3D Object Detection
- Paper: https://arxiv.org/abs/2109.02497
- Code: None
- Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
- Paper: https://arxiv.org/abs/2109.02499
- Code: None
- An End-to-End Transformer Model for 3D Object Detection
- Homepage: https://facebookresearch.github.io/3detr/
- Paper: https://arxiv.org/abs/2109.08141
- Code: https://github.com/facebookresearch/3detr
- RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection
- Paper: https://arxiv.org/abs/2103.10039
- Code: https://github.com/TuSimple/RangeDet
- Geometry-based Distance Decomposition for Monocular 3D Object Detection
- Paper: https://arxiv.org/abs/2104.03775
- Code: https://github.com/Rock-100/MonoDet
- 3D Semantic Segmentation(3D语义分割)
- ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation
- Paper: https://arxiv.org/abs/2107.11769
- Code: None
- Learning with Noisy Labels for Robust Point Cloud Segmentation
- Homepage: https://shuquanye.com/PNAL_website/
- Paper(Oral): https://arxiv.org/abs/2107.14230
- VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation
- Paper(Oral): https://arxiv.org/abs/2107.13824
- Code: https://github.com/hzykent/VMNet
- Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation
- Paper: https://arxiv.org/abs/2107.14724
- Code: https://github.com/leolyj/DsCML
- DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation
- Paper: https://arxiv.org/abs/2108.04023
- Code: None
- Adaptive Graph Convolution for Point Cloud Analysis
- Paper: https://arxiv.org/abs/2108.08035
- Code: https://github.com/hrzhou2/AdaptConv-master
- Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
- Paper: https://arxiv.org/abs/2106.15277
- Code: https://github.com/ICEORY/PMF
- 3D Instance Segmentation(3D实例分割)
- Hierarchical Aggregation for 3D Instance Segmentation
- Paper: https://arxiv.org/abs/2108.02350
- Code: https://github.com/hustvl/HAIS
- 3D Multi-Object Tracking(3D多目标跟踪)
- Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
- Paper: https://arxiv.org/abs/2108.10312
- Code: https://github.com/qcraftai/simtrack
- Point Cloud Denoising(点云去噪)
- Score-Based Point Cloud Denoising
- Paper: https://arxiv.org/abs/2107.10981
- Code: None
- Point Cloud Registration(点云配准)
- HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
- Homepage: https://ispc-group.github.io/hregnet
- Paper: https://arxiv.org/abs/2107.11992
- Code: https://github.com/ispc-lab/HRegNet
- A Robust Loss for Point Cloud Registration
- Paper: https://arxiv.org/abs/2108.11682
- Code: None
- Point Cloud Completion(点云补全)
- PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
- Paper(Oral): https://arxiv.org/abs/2108.08839
- Code: https://github.com/yuxumin/PoinTr
- SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
- Paper: https://arxiv.org/abs/2108.04444
- Code: https://github.com/AllenXiangX/SnowflakeNet
- 雷达语义分割(Radar Semantic Segmentation)
- Multi-View Radar Semantic Segmentation
- Paper: https://arxiv.org/abs/2103.16214
- Code: https://github.com/valeoai/MVRSS
- 图像恢复(Image Restoration)
- Dynamic Attentive Graph Learning for Image Restoration
- Paper: https://arxiv.org/abs/2109.06620
- Code: https://github.com/jianzhangcs/DAGL
- 超分辨率(Super-Resolution)
- Learning for Scale-Arbitrary Super-Resolution from Scale-Specific Networks
- Paper: https://arxiv.org/abs/2004.03791
- Code: https://github.com/LongguangWang/ArbSR
- Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
- Paper: https://arxiv.org/abs/2108.05302
- Code: https://github.com/JingyunLiang/MANet
- Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
- Paper(Oral): https://arxiv.org/abs/2108.08286
- Code: None
- Dual-Camera Super-Resolution with Aligned Attention Modules
- Homepage: https://tengfei-wang.github.io/Dual-Camera-SR/index.html
- Paper: https://arxiv.org/abs/2109.01349
- Code: https://github.com/Tengfei-Wang/DualCameraSR
- Dataset: https://tengfei-wang.github.io/Dual-Camera-SR/index.html
- Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme
- Paper: https://www4.comp.polyu.edu.hk/~cslzhang/paper/ICCV21_RealVSR.pdf
- Code: https://github.com/IanYeung/RealVSR
- Dataset: https://github.com/IanYeung/RealVSR
- 去噪(Denoising)
- Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
- Paper(Oral): https://arxiv.org/abs/2108.08286
- Code: None
- Rethinking Deep Image Prior for Denoising
- Paper: https://arxiv.org/abs/2108.12841
- Code: https://github.com/gistvision/DIP-denosing
- 医学图像去噪(Medical Image Denoising)
- Eformer: Edge Enhancement based Transformer for Medical Image Denoising
- Paper: https://arxiv.org/abs/2109.08044
- Code: None
- 去模糊(Deblurring)
- Rethinking Coarse-to-Fine Approach in Single Image Deblurring
- Paper: https://arxiv.org/abs/2108.05054
- Code: https://github.com/chosj95/MIMO-UNet
- Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions
- Paper: https://arxiv.org/abs/2108.09108
- Code: None
- 阴影去除(Shadow Removal)
- CANet: A Context-Aware Network for Shadow Removal
- Paper: https://arxiv.org/abs/2108.09894
- Code: https://github.com/Zipei-Chen/CANet
- 视频插帧(Video Frame Interpolation)
- XVFI: eXtreme Video Frame Interpolation
- Paper(Oral): https://arxiv.org/abs/2103.16206
- Code: https://github.com/JihyongOh/XVFI
- Dataset: https://github.com/JihyongOh/XVFI
- Asymmetric Bilateral Motion Estimation for Video Frame Interpolation
- Paper: https://arxiv.org/abs/2108.06815
- Code: https://github.com/JunHeum/ABME
- 视频修复/补全(Video Inpainting)
- FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
- Paper: https://arxiv.org/abs/2109.02974
- Code: https://github.com/ruiliu-ai/FuseFormer
- 行人重识别(Person Re-identification)
- TransReID: Transformer-based Object Re-Identification
- Paper: https://arxiv.org/abs/2102.04378
- Code: https://github.com/heshuting555/TransReID
- IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
- Paper(Oral): https://arxiv.org/abs/2108.02413
- Code: https://github.com/SikaStar/IDM
- 行人搜索(Person Search)
- Weakly Supervised Person Search with Region Siamese Networks
- Paper: https://arxiv.org/abs/2109.06109
- Code: None
- 2D/3D人体姿态估计(2D/3D Human Pose Estimation)
- 2D 人体姿态估计
- Human Pose Regression with Residual Log-likelihood Estimation
- Paper(Oral): https://arxiv.org/abs/2107.11291
- Code(RLE): https://github.com/Jeff-sjtu/res-loglikelihood-regression
- Online Knowledge Distillation for Efficient Pose Estimation
- Paper: https://arxiv.org/abs/2108.02092
- Code: None
- 3D 人体姿态估计
- Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows
- Paper: https://arxiv.org/abs/2107.13788
- Code: https://github.com/twehrbein/Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows
- Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images
- Paper: https://arxiv.org/abs/2109.05885
- Code: None
- 6D位姿估计(6D Object Pose Estimation)
- StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation
- Paper: https://arxiv.org/abs/2109.10115
- Code: None
- Dataset: None
- 3D人头重建(3D Head Reconstruction)
- H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction
- Homepage: https://crisalixsa.github.io/h3d-net/
- Paper: https://arxiv.org/abs/2107.12512
- 人脸识别(Face Recognition)
- SynFace: Face Recognition with Synthetic Data
- Paper: https://arxiv.org/abs/2108.07960
- Code: None
- Facial Expression Recognition(人脸表情识别)
- TransFER: Learning Relation-aware Facial Expression Representations with Transformers
- Paper: https://arxiv.org/abs/2108.11116
- Code: None
- 行为识别(Action Recognition)
- MGSampler: An Explainable Sampling Strategy for Video Action Recognition
- Paper: https://arxiv.org/abs/2104.09952
- Code: None
- Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
- Paper: https://arxiv.org/abs/2107.12213
- Code: https://github.com/Uason-Chen/CTR-GCN
- Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
- Paper: https://arxiv.org/abs/2108.02183
- Code: None
- Dynamic Network Quantization for Efficient Video Inference
- Homepage: https://cs-people.bu.edu/sunxm/VideoIQ/project.html
- Paper: https://arxiv.org/abs/2108.10394
- Code: https://github.com/sunxm2357/VideoIQ
- 时序动作定位(Temporal Action Localization)
- Enriching Local and Global Contexts for Temporal Action Localization
- Paper: https://arxiv.org/abs/2107.12960
- Code: None
- 动作检测(Action Detection)
- Class Semantics-based Attention for Action Detection
- Paper: https://arxiv.org/abs/2109.02613
- Code: None
- 群体活动识别(Group Activity Recognition)
- GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
- Paper: https://arxiv.org/abs/2108.12630
- Code: https://github.com/xueyee/GroupFormer
- 手语识别(Sign Language Recognition)
- Visual Alignment Constraint for Continuous Sign Language Recognition
- Paper: https://arxiv.org/abs/2104.02330
- Code: https://github.com/ycmin95/VAC_CSLR
- 文本检测(Text Detection)
- Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection
- Paper: https://arxiv.org/abs/2107.12664
- Code: https://github.com/GXYM/TextBPN
- 文本识别(Text Recognition)
- Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition
- Paper: https://arxiv.org/abs/2107.12090
- Code: None
- 文本替换(Text Replacement)
- STRIVE: Scene Text Replacement In Videos
- Homepage: https://striveiccv2021.github.io/STRIVE-ICCV2021/
- Paper: https://arxiv.org/abs/2109.02762
- Code: https://github.com/striveiccv2021/STRIVE-ICCV2021/
- Datasets: https://github.com/striveiccv2021/STRIVE-ICCV2021/
- 视觉问答(Visual Question Answering, VQA)
- Greedy Gradient Ensemble for Robust Visual Question Answering
- Paper: https://arxiv.org/abs/2107.12651
- Code: https://github.com/GeraldHan/GGE
- 对抗攻击(Adversarial Attack)
- Feature Importance-aware Transferable Adversarial Attacks
- Paper: https://arxiv.org/abs/2107.14185
- Code: https://github.com/hcguoO0/FIA
- AdvDrop: Adversarial Attack to DNNs by Dropping Information
- Paper: https://arxiv.org/abs/2108.09034
- Code: https://github.com/RjDuan/AdvDrop
- 深度估计(Depth Estimation)
- Augmenting Depth Estimation with Geospatial Context
- Paper: https://arxiv.org/abs/2109.09879
- Code: None
- NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo
- Paper(Oral): https://arxiv.org/abs/2109.01129
- Code: https://github.com/weiyithu/NerfingMVS
- 单目深度估计
- MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments
- Paper: https://arxiv.org/abs/2107.12429
- Code: None
- Towards Interpretable Deep Networks for Monocular Depth Estimation
- Paper: https://arxiv.org/abs/2108.05312
- Code: https://github.com/youzunzhi/InterpretableMDE
- Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark
- Paper: https://arxiv.org/abs/2108.03830
- Code: https://github.com/w2kun/RNW
- Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
- Paper: https://arxiv.org/abs/2108.07628
- Code: https://github.com/LINA-lln/ADDS-DepthNet
- StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation
- Paper: https://arxiv.org/abs/2108.08574
- Code: https://github.com/SJTU-ViSYS/StructDepth
- 视线估计(Gaze Estimation)
- Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation
- Paper: https://arxiv.org/abs/2107.13780
- Code: https://github.com/DreamtaleCore/PnP-GA
- 人群计数(Crowd Counting)
- Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework
- Paper(Oral): https://arxiv.org/abs/2107.12746
- Code(P2PNet): https://github.com/TencentYoutuResearch/CrowdCounting-P2PNet
- Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting
- Paper: https://arxiv.org/abs/2107.12619
- Code: https://github.com/TencentYoutuResearch/CrowdCounting-UEPNet
- 车道线检测(Lane-Detection)
- VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
- Paper: https://arxiv.org/abs/2108.08482
- Code: https://github.com/yujun0-0/MMA-Net
- Dataset: https://github.com/yujun0-0/MMA-Net
- 轨迹预测(Trajectory Prediction)
- Human Trajectory Prediction via Counterfactual Analysis
- Paper: https://arxiv.org/abs/2107.14202
- Code: https://github.com/CHENGY12/CausalHTP
- Personalized Trajectory Prediction via Distribution Discrimination
- Paper: https://arxiv.org/abs/2107.14204
- Code: https://github.com/CHENGY12/DisDis
- MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction
- Paper: https://arxiv.org/abs/2108.09274
- Code: https://github.com/selflein/MG-GAN
- Social NCE: Contrastive Learning of Socially-aware Motion Representations
- Paper: https://arxiv.org/abs/2012.11717
- Code: https://github.com/vita-epfl/social-nce
- Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
- Paper: https://arxiv.org/abs/2109.01510
- Code: https://github.com/xrenaa/Safety-Aware-Motion-Prediction
- 异常检测(Anomaly Detection)
- Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
- Paper: https://arxiv.org/abs/2101.10030
- Code: https://github.com/tianyu0207/RTFM
- 场景图生成(Scene Graph Generation)
- Spatial-Temporal Transformer for Dynamic Scene Graph Generation
- Paper: https://arxiv.org/abs/2107.12309
- Code: None
- 图像编辑(Image Editing)
- Sketch Your Own GAN
- Homepage: https://peterwang512.github.io/GANSketching/
- Paper: https://arxiv.org/abs/2108.02774
- 代码: https://github.com/peterwang512/GANSketching
- 图像合成(Image Synthesis)
- Image Synthesis via Semantic Composition
- Homepage: https://shepnerd.github.io/scg/
- Paper: https://arxiv.org/abs/2109.07053
- Code: https://github.com/dvlab-research/SCGAN
- 图像检索(Image Retrieval)
- Self-supervised Product Quantization for Deep Unsupervised Image Retrieval
- Paper: https://arxiv.org/abs/2109.02244
- Code: https://github.com/youngkyunJang/SPQ
- 三维重建(3D Reconstruction)
- Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
- Paper: https://arxiv.org/abs/2109.00512
- Code: https://github.com/facebookresearch/co3d
- Dataset: https://github.com/facebookresearch/co3d
- 视频稳像(Video Stabilization)
- Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization
- Paper: https://arxiv.org/abs/2108.09041
- 代码：https://github.com/Annbless/OVS_Stabilization
- 细粒度识别(Fine-Grained Recognition)
- Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
- Paper: https://arxiv.org/abs/2108.02399
- Code: https://github.com/NUST-Machine-Intelligence-Laboratory/weblyFG-dataset
- Dataset: https://github.com/NUST-Machine-Intelligence-Laboratory/weblyFG-dataset
- 风格迁移(Style Transfer)
- AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
- Paper: https://arxiv.org/abs/2108.03647
- Paddle Code：https://github.com/PaddlePaddle/PaddleGAN
- PyTorch Code：https://github.com/Huage001/AdaAttN
- 神经绘画(Neural Painting)
- Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
- Paper: https://arxiv.org/abs/2108.03798
- Code: https://github.com/wzmsltw/PaintTransformer
- 特征匹配(Feature Matching)
- Learning to Match Features with Seeded Graph Matching Network
- Paper: https://arxiv.org/abs/2108.08771
- Code: https://github.com/vdvchen/SGMNet
- 语义对应(Semantic Correspondence)
- Multi-scale Matching Networks for Semantic Correspondence
- Paper: https://arxiv.org/abs/2108.00211
- Code: https://github.com/wintersun661/MMNet
- 边缘检测(Edge Detection)
- Pixel Difference Networks for Efficient Edge Detection
- Paper: https://arxiv.org/abs/2108.07009
- Code: https://github.com/zhuoinoulu/pidinet
- RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth
- Paper: https://arxiv.org/abs/2108.00616
- Code : https://github.com/MengyangPu/RINDNet
- Dataset: https://github.com/MengyangPu/RINDNet
- 相机标定(Camera calibration)
- CTRL-C: Camera calibration TRansformer with Line-Classification
- Paper: https://arxiv.org/abs/2109.02259
- Code: https://github.com/jwlee-vcl/CTRL-C
- 图像质量评估(Image Quality Assessment)
- MUSIQ: Multi-scale Image Quality Transformer
- Paper: https://arxiv.org/abs/2108.05997
- Code: https://github.com/google-research/google-research/tree/master/musiq
- 度量学习(Metric Learning)
- Deep Relational Metric Learning
- Paper: https://arxiv.org/abs/2108.10026
- Code: https://github.com/zbr17/DRML
- Towards Interpretable Deep Metric Learning with Structural Matching
- Paper: https://arxiv.org/abs/2108.05889
- Code: https://github.com/wl-zhao/DIML
- Unsupervised Domain Adaptation
- Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation
- Paper(Oral): https://arxiv.org/abs/2107.13467
- Code: None
- Video Rescaling
- Self-Conditioned Probabilistic Learning of Video Rescaling
- Paper: https://arxiv.org/abs/2107.11639
- Code: None
- Hand-Object Interaction
- Learning a Contact Potential Field to Model the Hand-Object Interaction
- Paper: https://arxiv.org/abs/2012.00924
- Code: https://lixiny.github.io/CPF
- Vision-and-Language Navigation
- Airbert: In-domain Pretraining for Vision-and-Language Navigation
- Paper: https://arxiv.org/abs/2108.09105
- Code: https://airbert-vln.github.io/
- Dataset: https://airbert-vln.github.io/
- 数据集(Datasets)
- Beyond Road Extraction: A Dataset for Map Update using Aerial Images
- Homepage: https://favyen.com/muno21/
- Paper: https://arxiv.org/abs/2110.04690
- Code: https://github.com/favyen/muno21
- StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation
- Paper: https://arxiv.org/abs/2109.10115
- Code: None
- Dataset: None
- RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth
- Paper: https://arxiv.org/abs/2108.00616
- Code : https://github.com/MengyangPu/RINDNet
- Dataset: https://github.com/MengyangPu/RINDNet
- Panoptic Narrative Grounding
- Homepage: https://bcv-uniandes.github.io/panoptic-narrative-grounding/
- Paper(Oral): https://arxiv.org/abs/2109.04988
- Code: https://github.com/BCV-Uniandes/PNG
- Dataset: https://github.com/BCV-Uniandes/PNG
- STRIVE: Scene Text Replacement In Videos
- Homepage: https://striveiccv2021.github.io/STRIVE-ICCV2021/
- Paper: https://arxiv.org/abs/2109.02762
- Code: https://github.com/striveiccv2021/STRIVE-ICCV2021/
- Datasets: https://github.com/striveiccv2021/STRIVE-ICCV2021/
- Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme
- Paper: https://www4.comp.polyu.edu.hk/~cslzhang/paper/ICCV21_RealVSR.pdf
- Code: https://github.com/IanYeung/RealVSR
- Dataset: https://github.com/IanYeung/RealVSR
- Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes
- Paper: https://arxiv.org/abs/2109.03585
- Code: None
- Dual-Camera Super-Resolution with Aligned Attention Modules
- Homepage: https://tengfei-wang.github.io/Dual-Camera-SR/index.html
- Paper: https://arxiv.org/abs/2109.01349
- Code: https://github.com/Tengfei-Wang/DualCameraSR
- Dataset: https://tengfei-wang.github.io/Dual-Camera-SR/index.html
- DepthTrack: Unveiling the Power of RGBD Tracking
- Paper: https://arxiv.org/abs/2108.13962
- Code: https://github.com/xiaozai/DeT
- Dataset: https://github.com/xiaozai/DeT
- Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
- Paper: https://arxiv.org/abs/2109.00512
- Code: https://github.com/facebookresearch/co3d
- Dataset: https://github.com/facebookresearch/co3d
- BioFors: A Large Biomedical Image Forensics Dataset
- Paper: https://arxiv.org/abs/2108.12961
- Code: None
- Dataset: None
- Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
- Paper: https://arxiv.org/abs/2108.02399
- Code: https://github.com/NUST-Machine-Intelligence-Laboratory/weblyFG-dataset
- Dataset: https://github.com/NUST-Machine-Intelligence-Laboratory/weblyFG-dataset
- Airbert: In-domain Pretraining for Vision-and-Language Navigation
- Paper: https://arxiv.org/abs/2108.09105
- Code: https://airbert-vln.github.io/
- Dataset: https://airbert-vln.github.io/
- Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
- Paper: http://arxiv.org/abs/2108.08202
- Code: https://github.com/Neural-video-delivery/CaFM-Pytorch-ICCV2021
- Dataset: https://github.com/Neural-video-delivery/CaFM-Pytorch-ICCV2021
- VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
- Paper: https://arxiv.org/abs/2108.08482
- Code: https://github.com/yujun0-0/MMA-Net
- Dataset: https://github.com/yujun0-0/MMA-Net
- XVFI: eXtreme Video Frame Interpolation
- Paper(Oral): https://arxiv.org/abs/2103.16206
- Code: https://github.com/JihyongOh/XVFI
- Dataset: https://github.com/JihyongOh/XVFI
- Personalized Image Semantic Segmentation
- Paper: https://arxiv.org/abs/2107.13978
- Code: https://github.com/zhangyuygss/PIS
- Dataset: https://github.com/zhangyuygss/PIS
- H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction
- Homepage: https://crisalixsa.github.io/h3d-net/
- Paper: https://arxiv.org/abs/2107.12512
- 其他(Others)
- CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations
- Paper: https://arxiv.org/abs/2109.14910
- Code: None
- ReconfigISP: Reconfigurable Camera Image Processing Pipeline
- Paper: https://arxiv.org/abs/2109.04760
- Code: None
- Panoptic Narrative Grounding
- Homepage: https://bcv-uniandes.github.io/panoptic-narrative-grounding/
- Paper(Oral): https://arxiv.org/abs/2109.04988
- Code: https://github.com/BCV-Uniandes/PNG
- Dataset: https://github.com/BCV-Uniandes/PNG
- NEAT: Neural Attention Fields for End-to-End Autonomous Driving
- Paper: https://arxiv.org/abs/2109.04456
- https://github.com/autonomousvision/neat
- Keep CALM and Improve Visual Feature Attribution
- Paper: https://arxiv.org/abs/2106.07861
- Code: https://github.com/naver-ai/calm
- YouRefIt: Embodied Reference Understanding with Language and Gesture
- Paper: https://arxiv.org/abs/2109.03413
- Code: None
- Pri3D: Can 3D Priors Help 2D Representation Learning?
- Paper: https://arxiv.org/abs/2104.11225
- Code: https://github.com/Sekunde/Pri3D
- Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
- Paper: https://arxiv.org/abs/2108.08487
- Code: https://github.com/iCGY96/APR
- Continual Learning for Image-Based Camera Localization
- Paper: https://arxiv.org/abs/2108.09112
- Code: None
- Multi-Task Self-Training for Learning General Representations
- Paper: https://arxiv.org/abs/2108.11353
- Code: None
- A Unified Objective for Novel Class Discovery
- Homepage: https://ncd-uno.github.io/
- Paper(Oral): https://arxiv.org/abs/2108.08536
- Code: https://github.com/DonkeyShot21/UNO
- Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs
- Paper: https://arxiv.org/abs/2108.07884
- Code: https://github.com/islamamirul/PermuteNet
- Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
- Paper: http://arxiv.org/abs/2108.08202
- Code: https://github.com/Neural-video-delivery/CaFM-Pytorch-ICCV2021
- Dataset: https://github.com/Neural-video-delivery/CaFM-Pytorch-ICCV2021
- Impact of Aliasing on Generalizatin in Deep Convolutional Networks
- Paper: https://arxiv.org/abs/2108.03489
- Code: None
- Out-of-Core Surface Reconstruction via Global TGV Minimization
- Paper: https://arxiv.org/abs/2107.14790
- Code: None
- Progressive Correspondence Pruning by Consensus Learning
- Homepage: https://sailor-z.github.io/projects/CLNet.html
- Paper: https://arxiv.org/abs/2101.00591
- Code: https://github.com/sailor-z/CLNet
- Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
- Paper: https://arxiv.org/abs/2107.12628
- Code: None
- Generalized Shuffled Linear Regression
- Paper: https://drive.google.com/file/d/1Qu21VK5qhCW8WVjiRnnBjehrYVmQrDNh/view?usp=sharing
- Code: https://github.com/SILI1994/Generalized-Shuffled-Linear-Regression
- Discovering 3D Parts from Image Collections
- Homepage: https://chhankyao.github.io/lpd/
- Paper: https://arxiv.org/abs/2107.13629
- Semi-Supervised Active Learning with Temporal Output Discrepancy
- Paper: https://arxiv.org/abs/2107.14153
- Code: https://github.com/siyuhuang/TOD
- Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?
- Paper: https://arxiv.org/abs/2105.02498
- Code: https://github.com/KingJamesSong/DifferentiableSVD
- Hand-Object Contact Consistency Reasoning for Human Grasps Generation
- Homepage: https://hwjiang1510.github.io/GraspTTA/
- Paper(Oral): https://arxiv.org/abs/2104.03304
- Code: None
- Equivariant Imaging: Learning Beyond the Range Space
- Paper(Oral): https://arxiv.org/abs/2103.14756
- Code: https://github.com/edongdongchen/EI
- Just Ask: Learning to Answer Questions from Millions of Narrated Videos
- Paper(Oral): https://arxiv.org/abs/2012.00451
- Code: https://github.com/antoyang/just-ask-
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