https://github.com/52cv/wacv-2023-papers
https://github.com/52cv/wacv-2023-papers
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- Host: GitHub
- URL: https://github.com/52cv/wacv-2023-papers
- Owner: 52CV
- Created: 2022-08-23T07:26:54.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-01-16T08:10:06.000Z (over 3 years ago)
- Last Synced: 2025-01-06T05:09:37.826Z (over 1 year ago)
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- Forks: 4
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- Readme: README.md
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README
# 52CV-WACV-Papers
官网链接：[https://wacv2023.thecvf.com/home](https://wacv2023.thecvf.com/home)
会议日期：2023年1月3日-1月7日
## 历年综述论文分类汇总戳这里↘️[CV-Surveys](https://github.com/52CV/CV-Surveys)施工中~~~~~~~~~~
## 2023 年论文分类汇总戳这里
↘️[CVPR-2023-Papers](https://github.com/52CV/CVPR-2023-Papers)
↘️[WACV-2023-Papers](https://github.com/52CV/WACV-2023-Papers)
## 2022 年论文分类汇总戳这里
↘️[CVPR-2022-Papers](https://github.com/52CV/CVPR-2022-Papers)
↘️[WACV-2022-Papers](https://github.com/52CV/WACV-2022-Papers)
↘️[ECCV-2022-Papers](https://github.com/52CV/ECCV-2022-Papers)
## 2021年论文分类汇总戳这里
↘️[ICCV-2021-Papers](https://github.com/52CV/ICCV-2021-Papers)
↘️[CVPR-2021-Papers](https://github.com/52CV/CVPR-2021-Papers)
## 2020 年论文分类汇总戳这里
↘️[CVPR-2020-Papers](https://github.com/52CV/CVPR-2020-Papers)
↘️[ECCV-2020-Papers](https://github.com/52CV/ECCV-2020-Papers)
# :exclamation::exclamation::exclamation::star2::star2::star2:WACV 2023收录论文已全部公布，下载可在【我爱计算机视觉】后台回复“paper”，即可收到。共计 638 篇。
# 目录
|:dog:|:mouse:|:hamster:|:tiger:|
|---------|---------|---------|---------|
|[65.Open Set Recognition(开集识别)](#65)|[66.Scene Flow Estimation(场景流估计)](#66)|[67.Sketches(草图识别)](#67)|
|[61.geo-localization(城市地理定位)](#61)|[62.Dense Prediction(密集预测)](#62)|[63.Place Recognition(位置识别)](#63)|[64.Visual Odometry(视觉里程计)](#64)|
|[57.Federated Learning(联邦学习)](#57)|[58.HOI(人物交互)](#58)|[59.Meta learning(元学习)](#59)|[60.Image-to-Image Translation(图像-图像翻译)](#60)|
|[53.Gaze Estimation(视线估计)](#53)|[54.Optical Flow(光流)](#54)|[55.Clustering(聚类)](#55)|[56.Vision-Language(视觉语言)](#56)|
|[49.Neural Radiance(渲染)](#49)|[50.Contrastive Learning(对比学习)](#50)|[51.SGG(场景图生成)](#51)|[52.Human Motion Prediction(人类运动预测)](#52)|
|[45.Class-Incremental Learning(类增量学习)](#45)|[46.Metric Learning(度量学习)](#46)|[47.Data Augmentation(数据增强)](#47)|[48.Light Fields(光场)](#48)|
|[41.Action Generation(动作生成)](#41)|[42.Landmark Detection(关键点检测)](#42)|[43.Active Learning(主动学习)](#43)|[44.Multi-Task Learning(多任务学习)](#44)|
|[37.OT(目标跟踪)](#37)|[38.Sound(音频处理)](#38)|[39.Style Transfer(风格迁移)](#39)|[40.AD(异常检测)](#40)|
|[33.View Synthesis(视图合成)](#33)|[34.SLAM\Robots](#34)|[35.VQA(视觉问答)](#35)|[36.Soft Biometrics(软生物技术)](#36)|
|[29.Image Classification(图像分类)](#29)|[30.RL(强化学习)](#30)|[31.Deepfake Detection(假象检测)](#31)|[32.Continual Learning(持续学习)](#32)|
|[25.Image Captioning(图像字幕)](#25)|[26.Dataset(数据集)](#26)|[27.Person ReID(人员重识别)](#27)|[28.OPE(物体姿态估计)](#28)|
|[21.PC(点云)](#21)|[22.HAR(人体动作识别与检测)](#22)|[23.AD(智能驾驶)](#23)|[24.Image Retrieval(图像检索)](#24)|
|[17.OCR(文本检测)](#17)|[18.NAS(神经架构搜索)](#18)|[19.MC\KD\Pruning(模型压缩\知识蒸馏\剪枝)](#19)|[20.Transformer](#20)|
|[13.Image Segmentation(图像分割)](#13)|[14.SSL(半监督学习)](#14)|[15.Image Synthesis(图像合成)](#15)|[16.SR(超分辨率)](#16)|
|[9.RS\Satellite Image(遥感\卫星图像)](#9)|[10.AL(对抗学习)](#10)|[11.Face(人脸)](#11)|[12.FSL or DA\G(小样本学习 or 域适应\泛化)](#12)|
|[5.OD(目标检测)](#5)|[6.Video(视频相关)](#6)|[7.Pose(人体姿态)](#7)|[8.Image Processing(图像处理)](#8)|
|[1.其它](#1)|[2.Medical Image(医学影像)](#2)|[3.3D(三维视觉)](#3)|[4.GAN(生成对抗网络)](#4)|
## 67.Sketches(草图识别)
* [WHFL: Wavelet-Domain High Frequency Loss for Sketch-to-Image Translation](https://openaccess.thecvf.com/content/WACV2023/papers/Kim_WHFL_Wavelet-Domain_High_Frequency_Loss_for_Sketch-to-Image_Translation_WACV_2023_paper.pdf)
## 66.Scene Flow Estimation(场景流估计)
* [M-FUSE: Multi-frame Fusion for Scene Flow Estimation](https://openaccess.thecvf.com/content/WACV2023/papers/Mehl_M-FUSE_Multi-Frame_Fusion_for_Scene_Flow_Estimation_WACV_2023_paper.pdf)
:star:[code](https://github.com/cv-stuttgart/M-FUSE)
## 65.Open Set Recognition(开集识别)
* [Ancestor Search: Generalized Open Set Recognition via Hyperbolic Side Information Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Dengxiong_Ancestor_Search_Generalized_Open_Set_Recognition_via_Hyperbolic_Side_Information_WACV_2023_paper.pdf)
## 64.Visual Odometry(视觉里程计)
* [Pixel-Wise Prediction Based Visual Odometry via Uncertainty Estimation](https://openaccess.thecvf.com/content/WACV2023/papers/Chen_Pixel-Wise_Prediction_Based_Visual_Odometry_via_Uncertainty_Estimation_WACV_2023_paper.pdf)
## 63.Place Recognition(位置识别)
* [ETR: An Efficient Transformer for Re-ranking in Visual Place Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_ETR_An_Efficient_Transformer_for_Re-Ranking_in_Visual_Place_Recognition_WACV_2023_paper.pdf)
* [MixVPR: Feature Mixing for Visual Place Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Ali-bey_MixVPR_Feature_Mixing_for_Visual_Place_Recognition_WACV_2023_paper.pdf)
## 62.Dense Prediction(密集预测)
* [Dense Prediction With Attentive Feature Aggregation](https://openaccess.thecvf.com/content/WACV2023/papers/Yang_Dense_Prediction_With_Attentive_Feature_Aggregation_WACV_2023_paper.pdf)
## 61.geo-localization(城市地理定位)
* [TransVLAD: Multi-Scale Attention-Based Global Descriptors for Visual Geo-Localization](https://openaccess.thecvf.com/content/WACV2023/papers/Xu_TransVLAD_Multi-Scale_Attention-Based_Global_Descriptors_for_Visual_Geo-Localization_WACV_2023_paper.pdf)
:star:[code](https://github.com/wacv-23/TVLAD)
## 60.Image-to-Image Translation(图像-图像翻译)
* [Panoptic-Aware Image-to-Image Translation](https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Panoptic-Aware_Image-to-Image_Translation_WACV_2023_paper.pdf)
* 图像翻译
* [RIFT: Disentangled Unsupervised Image Translation via Restricted Information Flow](https://openaccess.thecvf.com/content/WACV2023/papers/Usman_RIFT_Disentangled_Unsupervised_Image_Translation_via_Restricted_Information_Flow_WACV_2023_paper.pdf)
* 域到域翻译
* [Learning Style Subspaces for Controllable Unpaired Domain Translation](https://openaccess.thecvf.com/content/WACV2023/papers/Bhatt_Learning_Style_Subspaces_for_Controllable_Unpaired_Domain_Translation_WACV_2023_paper.pdf)
:star:[code](https://github.com/GauravBh1010tt/Controllable-Domain-Translation)
## 59.Meta learning(元学习)
* [Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding](https://openaccess.thecvf.com/content/WACV2023/papers/Wang_Meta-OLE_Meta-Learned_Orthogonal_Low-Rank_Embedding_WACV_2023_paper.pdf)
## 58.Human Object Interaction(人物交互)
* [Skew-Robust Human-Object Interactions in Videos](https://openaccess.thecvf.com/content/WACV2023/papers/Agarwal_Skew-Robust_Human-Object_Interactions_in_Videos_WACV_2023_paper.pdf)
* 手物交互
* [Fine-grained Affordance Annotation for Egocentric Hand-Object Interaction Videos](https://openaccess.thecvf.com/content/WACV2023/papers/Yu_Fine-Grained_Affordance_Annotation_for_Egocentric_Hand-Object_Interaction_Videos_WACV_2023_paper.pdf)
## 57.Federated Learning(联邦学习)
* [Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning](https://arxiv.org/abs/2210.02326)
:star:[code](https://github.com/Erosinho13/LADD)
* [Federated Learning for Commercial Image Sources](https://openaccess.thecvf.com/content/WACV2023/papers/Jain_Federated_Learning_for_Commercial_Image_Sources_WACV_2023_paper.pdf)
## 56.Vision-Language(视觉语言)
* [VL-Taboo: An Analysis of Attribute-based Zero-shot Capabilities of Vision-Language Models](https://arxiv.org/abs/2209.06103)
:star:[code](https://github.com/felixVogel02/VL-Taboo/tree/main)
* [Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision](https://arxiv.org/abs/2210.13591)
* [Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention](https://arxiv.org/abs/2211.11701)
:star:[code](https://github.com/zinengtang/Perceiver_VL)
* [GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal ownstream tasks](https://openaccess.thecvf.com/content/WACV2023/papers/Susladkar_GAFNet_A_Global_Fourier_Self_Attention_Based_Novel_Network_for_WACV_2023_paper.pdf)
* VLN
* [Structure-Encoding Auxiliary Tasks for Improved Visual Representation in Vision-and-Language Navigatio](https://openaccess.thecvf.com/content/WACV2023/papers/Kuo_Structure-Encoding_Auxiliary_Tasks_for_Improved_Visual_Representation_in_Vision-and-Language_Navigation_WACV_2023_paper.pdf)
* [A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues](https://openaccess.thecvf.com/content/WACV2023/papers/Armitage_A_Priority_Map_for_Vision-and-Language_Navigation_With_Trajectory_Plans_and_WACV_2023_paper.pdf)
:star:[code](https://github.com/JasonArmitage-res/PM-VLN)
## 55.Clustering(聚类)
* [Self-Supervised Clustering based on Manifold Learning and Graph Convolutional Networks](https://openaccess.thecvf.com/content/WACV2023/papers/Lopes_Self-Supervised_Clustering_Based_on_Manifold_Learning_and_Graph_Convolutional_Networks_WACV_2023_paper.pdf)
:star:[code](https://github.com/lopes-leonardo/sgcc)
## 54.Optical Flow(光流)
* [Weakly-Supervised Optical Flow Estimation for Time-of-Flight](https://arxiv.org/abs/2210.05298)
* [Rebalancing Gradient To Improve Self-Supervised Co-Training of Depth, Odometry and Optical Flow Predictions](https://openaccess.thecvf.com/content/WACV2023/papers/Hariat_Rebalancing_Gradient_To_Improve_Self-Supervised_Co-Training_of_Depth_Odometry_and_WACV_2023_paper.pdf)
:star:[code](https://github.com/mhariat/CoopNet)
* [DCVNet: Dilated Cost Volume Networks for Fast Optical Flow](https://openaccess.thecvf.com/content/WACV2023/papers/Jiang_DCVNet_Dilated_Cost_Volume_Networks_for_Fast_Optical_Flow_WACV_2023_paper.pdf)
* [MFCFlow : A Motion Feature Compensated Multi-Frame Recurrent Network for Optical Flow Estimation](https://openaccess.thecvf.com/content/WACV2023/papers/Chen_MFCFlow_A_Motion_Feature_Compensated_Multi-Frame_Recurrent_Network_for_Optical_WACV_2023_paper.pdf)
* [BrightFlow: Brightness-Change-Aware Unsupervised Learning of Optical Flow](https://openaccess.thecvf.com/content/WACV2023/papers/Marsal_BrightFlow_Brightness-Change-Aware_Unsupervised_Learning_of_Optical_Flow_WACV_2023_paper.pdf)
:star:[code](https://github.com/CEA-LIST/BrightFlow)
* [Towards Equivariant Optical Flow Estimation with Deep Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Savian_Towards_Equivariant_Optical_Flow_Estimation_With_Deep_Learning_WACV_2023_paper.pdf)(https://github.com/stsavian/equivariant_of_estimation)
* [Learning Lightweight Neural Networks via Channel-Split Recurrent Convolution](https://openaccess.thecvf.com/content/WACV2023/papers/Figueiredo_Frame_Interpolation_for_Dynamic_Scenes_With_Implicit_Flow_Encoding_WACV_2023_paper.pdf)
* [Meta-Learning for Adaptation of Deep Optical Flow Networks](https://openaccess.thecvf.com/content/WACV2023/papers/Min_Meta-Learning_for_Adaptation_of_Deep_Optical_Flow_Networks_WACV_2023_paper.pdf)
## 53.Gaze Estimation(视线估计)
* [Searching Efficient Neural Architecture with Multi-resolution Fusion Transformer for Appearance-based Gaze Estimation](https://openaccess.thecvf.com/content/WACV2023/papers/Nagpure_Searching_Efficient_Neural_Architecture_With_Multi-Resolution_Fusion_Transformer_for_Appearance-Based_WACV_2023_paper.pdf)
* iris localization(虹膜定位)
* [Segmentation-free Direct Iris Localization Networks](https://arxiv.org/abs/2210.10403)
* 视线跟随
* [Patch-level Gaze Distribution Prediction for Gaze Following](https://arxiv.org/abs/2211.11062)
* 视线重定向
* [Fine Gaze Redirection Learning with Gaze Hardness-aware Transformation](https://openaccess.thecvf.com/content/WACV2023/papers/Park_Fine_Gaze_Redirection_Learning_With_Gaze_Hardness-Aware_Transformation_WACV_2023_paper.pdf)
:star:[code](https://github.com/san9569/Gaze-Redir-Learning)
* [CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head Redirection](https://openaccess.thecvf.com/content/WACV2023/papers/Jindal_CUDA-GHR_Controllable_Unsupervised_Domain_Adaptation_for_Gaze_and_Head_Redirection_WACV_2023_paper.pdf)
:star:[code](https://github.com/jswati31/cuda-ghr)
## 52.Human Motion Prediction(人类运动预测)
* [Multi-view Tracking Using Weakly Supervised Human Motion Prediction](https://arxiv.org/abs/2210.10771)
:star:[code](https://github.com/cvlab-epfl/MVFlow)
* [Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation](https://arxiv.org/abs/2210.12649)
:star:[code](https://github.com/zeyun-zhong/AFFT)
* [GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction](https://arxiv.org/abs/2210.13605)
* [Back to MLP: A Simple Baseline for Human Motion Prediction](https://openaccess.thecvf.com/content/WACV2023/papers/Guo_Back_to_MLP_A_Simple_Baseline_for_Human_Motion_Prediction_WACV_2023_paper.pdf)
:star:[code](https://github.com/dulucas/siMLPe)
* [Intention-Conditioned Long-Term Human Egocentric Action Anticipation](https://openaccess.thecvf.com/content/WACV2023/papers/Mascaro_Intention-Conditioned_Long-Term_Human_Egocentric_Action_Anticipation_WACV_2023_paper.pdf)
* 行人轨迹预测
* [Online Adaptive Temporal Memory with Certainty Estimation for Human Trajectory Prediction](https://openaccess.thecvf.com/content/WACV2023/papers/Huynh_Online_Adaptive_Temporal_Memory_With_Certainty_Estimation_for_Human_Trajectory_WACV_2023_paper.pdf)
## 51.Scene Graph Generation(场景图生成)
* [Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing](https://arxiv.org/abs/2211.01969)
:star:[code](https://github.com/IISCAditayTripathi/Scene-graph-localization):house:[project](https://iiscaditaytripathi.github.io/sgl/)
* [Improving Predicate Representation in Scene Graph Generation by Self-Supervised Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Hasegawa_Improving_Predicate_Representation_in_Scene_Graph_Generation_by_Self-Supervised_Learning_WACV_2023_paper.pdf)
* [More Knowledge, Less Bias: Unbiasing Scene Graph Generation with Explicit Ontological Adjustment](https://openaccess.thecvf.com/content/WACV2023/papers/Chen_More_Knowledge_Less_Bias_Unbiasing_Scene_Graph_Generation_With_Explicit_WACV_2023_paper.pdf)
:star:[code](https://github.com/zhanwenchen/eoa)
* [Composite Relationship Fields with Transformers for Scene Graph Generation](https://openaccess.thecvf.com/content/WACV2023/papers/Adaimi_Composite_Relationship_Fields_With_Transformers_for_Scene_Graph_Generation_WACV_2023_paper.pdf)
:star:[code](https://github.com/vita-epfl/SGG-CoRF)
## 50.Contrastive Learning(对比学习)
* [Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Denize_Similarity_Contrastive_Estimation_for_Self-Supervised_Soft_Contrastive_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/CEA-LIST/SCE)
* [Representation Disentanglement in Generative Models with Contrastive Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Mo_Representation_Disentanglement_in_Generative_Models_With_Contrastive_Learning_WACV_2023_paper.pdf)
* [Addressing Feature Suppression in Unsupervised Visual Representations](https://openaccess.thecvf.com/content/WACV2023/papers/Li_Addressing_Feature_Suppression_in_Unsupervised_Visual_Representations_WACV_2023_paper.pdf)
* [Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Noguchi_Ego-Vehicle_Action_Recognition_Based_on_Semi-Supervised_Contrastive_Learning_WACV_2023_paper.pdf)
## 49.Neural Radiance(渲染)
* [Ev-NeRF: Event Based Neural Radiance Field](https://openaccess.thecvf.com/content/WACV2023/papers/Li_Jointly_Learning_Band_Selection_and_Filter_Array_Design_for_Hyperspectral_WACV_2023_paper.pdf)
* [DDNeRF: Depth Distribution Neural Radiance Fields](https://openaccess.thecvf.com/content/WACV2023/papers/Dadon_DDNeRF_Depth_Distribution_Neural_Radiance_Fields_WACV_2023_paper.pdf)
* [X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360deg Insufficient RGB-D Views](https://openaccess.thecvf.com/content/WACV2023/papers/Zhu_X-NeRF_Explicit_Neural_Radiance_Field_for_Multi-Scene_360deg_Insufficient_RGB-D_WACV_2023_paper.pdf)
:star:[code](https://github.com/HaoyiZhu/XNeRF)
* [Fast Differentiable Transient Rendering for Non-Line-of-Sight Reconstruction](https://openaccess.thecvf.com/content/WACV2023/papers/Plack_Fast_Differentiable_Transient_Rendering_for_Non-Line-of-Sight_Reconstruction_WACV_2023_paper.pdf)
:star:[code](https://github.com/unlikelymaths/totrilib)
* [Compressing Explicit Voxel Grid Representations: fast NeRFs become also small](https://openaccess.thecvf.com/content/WACV2023/papers/Deng_Compressing_Explicit_Voxel_Grid_Representations_Fast_NeRFs_Become_Also_Small_WACV_2023_paper.pdf)
* [Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation](https://openaccess.thecvf.com/content/WACV2023/papers/Lazova_Control-NeRF_Editable_Feature_Volumes_for_Scene_Rendering_and_Manipulation_WACV_2023_paper.pdf)
:house:[project](https://virtualhumans.mpi-inf.mpg.de/control-nerf/)
* [Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields](https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Beyond_RGB_Scene-Property_Synthesis_With_Neural_Radiance_Fields_WACV_2023_paper.pdf)
:star:[code](https://github.com/zsh2000/SS-NeRF)
## 48.Light Fields(光场)
* 光场
* [I See-Through You: A Framework for Removing Foreground Occlusion in Both Sparse and Dense Light Field Images](https://openaccess.thecvf.com/content/WACV2023/papers/Hur_I_See-Through_You_A_Framework_for_Removing_Foreground_Occlusion_in_WACV_2023_paper.pdf)
* 相机
* [Event-based RGB sensing with structured light](https://openaccess.thecvf.com/content/WACV2023/papers/Bajestani_Event-Based_RGB_Sensing_With_Structured_Light_WACV_2023_paper.pdf)
:star:[code](https://github.com/MISTLab/event_based_rgbd_ros)
* [Burst Vision Using Single-Photon Cameras](https://openaccess.thecvf.com/content/WACV2023/papers/Ma_Burst_Vision_Using_Single-Photon_Cameras_WACV_2023_paper.pdf)
* [TVCalib: Camera Calibration for Sports Field Registration in Soccer](https://openaccess.thecvf.com/content/WACV2023/papers/Theiner_TVCalib_Camera_Calibration_for_Sports_Field_Registration_in_Soccer_WACV_2023_paper.pdf)
:house:[project](https://mm4spa.github.io/tvcalib/)
* 兴趣点检测
* [EventPoint: Self-Supervised Interest Point Detection and Description for Event-Based Camera](https://openaccess.thecvf.com/content/WACV2023/papers/Huang_EventPoint_Self-Supervised_Interest_Point_Detection_and_Description_for_Event-Based_Camera_WACV_2023_paper.pdf)
## 47.Data Augmentation(数据增强)
* [Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation](https://arxiv.org/abs/2210.12681)
:star:[code](https://github.com/AtsuMiyai/rethinking_rotation)
## 46.Metric Learning(度量学习)
* [InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images](https://arxiv.org/abs/2211.12760)
:star:[code](https://github.com/LSX-UniWue/InDiReCT)
## 45.Class-Incremental Learning(类增量学习)
* [AdvisIL - A Class-Incremental Learning Advisor](https://openaccess.thecvf.com/content/WACV2023/papers/Feillet_AdvisIL_-_A_Class-Incremental_Learning_Advisor_WACV_2023_paper.pdf)
:star:[code](https://github.com/EvaJF/AdvisIL)
* [FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Petit_FeTrIL_Feature_Translation_for_Exemplar-Free_Class-Incremental_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/GregoirePetit/FeTrIL)
* 增量学习
* [Neural Weight Search for Scalable Task Incremental Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Jiang_Neural_Weight_Search_for_Scalable_Task_Incremental_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/JianJiangKCL/NeuralWeightSearch)
## 44.Multi-Task Learning(多任务学习)
* [Cross-task Attention Mechanism for Dense Multi-task Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Lopes_Cross-Task_Attention_Mechanism_for_Dense_Multi-Task_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/astra-vision/DenseMTL)
## 43.Active Learning(主动学习)
* [Randomness is the Root of All Evil:More Reliable Evaluation of Deep Active Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Ji_Randomness_Is_the_Root_of_All_Evil_More_Reliable_Evaluation_WACV_2023_paper.pdf)
:house:[project](https://intellisec.de/research/eval-al)
## 42.Landmark Detection(关键点检测)
* [CoKe: Contrastive Learning for Robust Keypoint Detection](https://openaccess.thecvf.com/content/WACV2023/papers/Bai_CoKe_Contrastive_Learning_for_Robust_Keypoint_Detection_WACV_2023_paper.pdf)
## 41.Action Generation(动作生成)
* 全身运动合成
* [DSAG: A Scalable Deep Framework for Action-Conditioned Multi-Actor Full Body Motion Synthesis](https://openaccess.thecvf.com/content/WACV2023/papers/Gupta_DSAG_A_Scalable_Deep_Framework_for_Action-Conditioned_Multi-Actor_Full_Body_WACV_2023_paper.pdf)
## 40.Anomaly Detection(异常检测)
* [Asymmetric Student-Teacher Networks for Industrial Anomaly Detection](https://arxiv.org/abs/2210.07829)
:star:[code](https://github.com/marco-rudolph/ast)
* [Zero-Shot Versus Many-Shot: Unsupervised Texture Anomaly Detection](https://openaccess.thecvf.com/content/WACV2023/papers/Aota_Zero-Shot_Versus_Many-Shot_Unsupervised_Texture_Anomaly_Detection_WACV_2023_paper.pdf)
:star:[code](https://drive.google.com/drive/folders/10OyPzvI3H6llCZBxKxFlKWt1Pw1tkMK1)
* [No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection](https://openaccess.thecvf.com/content/WACV2023/papers/Yousef_No_Shifted_Augmentations_NSA_Compact_Distributions_for_Robust_Self-Supervised_Anomaly_WACV_2023_paper.pdf)
* [GLAD: A Global-to-Local Anomaly Detector](https://openaccess.thecvf.com/content/WACV2023/papers/Artola_GLAD_A_Global-to-Local_Anomaly_Detector_WACV_2023_paper.pdf)
* 道路异常检测
* [Image-Consistent Detection of Road Anomalies as Unpredictable Patches](https://openaccess.thecvf.com/content/WACV2023/papers/Vojir_Image-Consistent_Detection_of_Road_Anomalies_As_Unpredictable_Patches_WACV_2023_paper.pdf)
* 异常聚类
* [Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types](https://openaccess.thecvf.com/content/WACV2023/papers/Sohn_Anomaly_Clustering_Grouping_Images_Into_Coherent_Clusters_of_Anomaly_Types_WACV_2023_paper.pdf)
## 39.Style Transfer(风格迁移)
* [Line Search-Based Feature Transformation for Fast, Stable, and Tunable Content-Style Control in Photorealistic Style Transfer](https://arxiv.org/abs/2210.05996)
:star:[code](https://github.com/chiutaiyin/LS-FT)
* [RAST: Restorable Arbitrary Style Transfer via Multi-Restoration](https://openaccess.thecvf.com/content/WACV2023/papers/Ma_RAST_Restorable_Arbitrary_Style_Transfer_via_Multi-Restoration_WACV_2023_paper.pdf)
* [Dance Style Transfer with Cross-modal Transformer](https://openaccess.thecvf.com/content/WACV2023/papers/Yin_Dance_Style_Transfer_With_Cross-Modal_Transformer_WACV_2023_paper.pdf)
:tv:[video](https://www.youtube.com/watch?v=kP4DBp8OUCk)
* [Is Bigger Always Better? An Empirical Study on Efficient Architectures for Style Transfer and Beyond](https://openaccess.thecvf.com/content/WACV2023/papers/An_Is_Bigger_Always_Better_An_Empirical_Study_on_Efficient_Architectures_WACV_2023_paper.pdf)
## 38.Sound(音频处理)
* [AudioViewer: Learning to Visualize Sounds](https://openaccess.thecvf.com/content/WACV2023/papers/Song_AudioViewer_Learning_To_Visualize_Sounds_WACV_2023_paper.pdf)
:house:[project](https://chunjinsong.github.io/audioviewer)
* Audio Visual Event Localization视听事件定位
* [AVE-CLIP: AudioCLIP-based Multi-window Temporal Transformer for Audio Visual Event Localization](https://arxiv.org/abs/2210.05060)
* 音频去噪
* [BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds](https://arxiv.org/abs/2210.10196)
* 视听分割
* [Unsupervised Audio-Visual Lecture Segmentation](https://arxiv.org/abs/2210.16644)
:house:[project](https://cvit.iiit.ac.in/research/projects/cvit-projects/avlectures)
* 生源定位
* [Hear The Flow: Optical Flow-Based Self-Supervised Visual Sound Source Localization](https://arxiv.org/abs/2211.03019)
:star:[code](https://github.com/denfed/heartheflow)
* [Exploiting Visual Context Semantics for Sound Source Localization](https://openaccess.thecvf.com/content/WACV2023/papers/Zhou_Exploiting_Visual_Context_Semantics_for_Sound_Source_Localization_WACV_2023_paper.pdf)
* 语音识别
* [Audio-Visual Efficient Conformer for Robust Speech Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Burchi_Audio-Visual_Efficient_Conformer_for_Robust_Speech_Recognition_WACV_2023_paper.pdf)
:star:[code](https://github.com/burchim/AVEC)
* 音频分离
* [SeCo: Separating Unknown Musical Visual Sounds with Consistency Guidance](https://openaccess.thecvf.com/content/WACV2023/papers/Zhou_SeCo_Separating_Unknown_Musical_Visual_Sounds_With_Consistency_Guidance_WACV_2023_paper.pdf)
## 37.Object Tracking(目标跟踪)
* [Efficient Visual Tracking with Exemplar Transformers](https://arxiv.org/abs/2112.09686)
:star:[code](https://github.com/pblatter/ettrack)
* [Hard to Track Objects with Irregular Motions and Similar Appearances?Make It Easier by Buffering the Matching Space](https://openaccess.thecvf.com/content/WACV2023/papers/Yang_Hard_To_Track_Objects_With_Irregular_Motions_and_Similar_Appearances_WACV_2023_paper.pdf)
* [HOOT: Heavy Occlusions in Object Tracking Benchmark](https://openaccess.thecvf.com/content/WACV2023/papers/Sahin_HOOT_Heavy_Occlusions_in_Object_Tracking_Benchmark_WACV_2023_paper.pdf)
* [VirtualHome Action Genome: A Simulated Spatio-Temporal Scene Graph Dataset With Consistent Relationship Labels](https://openaccess.thecvf.com/content/WACV2023/papers/Qiu_VirtualHome_Action_Genome_A_Simulated_Spatio-Temporal_Scene_Graph_Dataset_With_WACV_2023_paper.pdf)
* [Tracking Growth and Decay of Plant Roots in Minirhizotron Images](https://openaccess.thecvf.com/content/WACV2023/papers/Gillert_Tracking_Growth_and_Decay_of_Plant_Roots_in_Minirhizotron_Images_WACV_2023_paper.pdf)
:star:[code](https://github.com/alexander-g/Root-Tracking)
* [Planar Object Tracking via Weighted Optical Flow](https://openaccess.thecvf.com/content/WACV2023/papers/Serych_Planar_Object_Tracking_via_Weighted_Optical_Flow_WACV_2023_paper.pdf)
* [Multi-Frame Attention with Feature-Level Warping for Drone Crowd Tracking](https://openaccess.thecvf.com/content/WACV2023/papers/Asanomi_Multi-Frame_Attention_With_Feature-Level_Warping_for_Drone_Crowd_Tracking_WACV_2023_paper.pdf)
* 多目标跟踪
* [AttTrack: Online Deep Attention Transfer for Multi-object Tracking](https://arxiv.org/abs/2210.08648)
* [Detection Recovery in Online Multi-Object Tracking With Sparse Graph Tracker](https://openaccess.thecvf.com/content/WACV2023/papers/Hyun_Detection_Recovery_in_Online_Multi-Object_Tracking_With_Sparse_Graph_Tracker_WACV_2023_paper.pdf)
:star:[code](https://github.com/HYUNJS/SGT)
* [MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark](https://openaccess.thecvf.com/content/WACV2023/papers/Han_MMPTRACK_Large-Scale_Densely_Annotated_Multi-Camera_Multiple_People_Tracking_Benchmark_WACV_2023_paper.pdf)
* [TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking](https://openaccess.thecvf.com/content/WACV2023/papers/Chu_TransMOT_Spatial-Temporal_Graph_Transformer_for_Multiple_Object_Tracking_WACV_2023_paper.pdf)
## 36.Soft Biometrics(软生物技术)
* 手指静脉识别
* [Analysis of Master Vein Attacks on Finger Vein Recognition Systems](https://arxiv.org/abs/2210.10667)
* 隐形眼镜虹膜PAD算法的错误分类
* [Misclassifications of Contact Lens Iris PAD Algorithms: Is it Gender Bias or Environmental Conditions](https://openaccess.thecvf.com/content/WACV2023/papers/Agarwal_Misclassifications_of_Contact_Lens_Iris_PAD_Algorithms_Is_It_Gender_WACV_2023_paper.pdf)
* 生物信息识别
* [Can Shadows Reveal Biometric Information?](https://openaccess.thecvf.com/content/WACV2023/papers/Medin_Can_Shadows_Reveal_Biometric_Information_WACV_2023_paper.pdf)
* 虹膜
* [DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation](https://openaccess.thecvf.com/content/WACV2023/papers/Khan_DeformIrisNet_An_Identity-Preserving_Model_of_Iris_Texture_Deformation_WACV_2023_paper.pdf)
:star:[code](https://github.com/CVRL/DeformIrisNet)
## 35.VQA(视觉问答)
* [DRAMA: Joint Risk Localization and Captioning in Driving](https://arxiv.org/abs/2209.10767)
* [VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge](https://arxiv.org/abs/2210.13626)
:star:[code](https://github.com/aditya10/VLC-BERT)
* [Barlow constrained optimization for Visual Question Answering](https://openaccess.thecvf.com/content/WACV2023/papers/Jha_Barlow_Constrained_Optimization_for_Visual_Question_Answering_WACV_2023_paper.pdf)
:star:[code](https://github.com/abskjha/Barlow-constrained-VQA)
* [How To Practice VQA on a Resource-Limited Target Domain](https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_How_To_Practice_VQA_on_a_Resource-Limited_Target_Domain_WACV_2023_paper.pdf)
:house:[project](https://people.cs.pitt.edu/~mzhang/practice-vqa/)
* [Guiding Visual Question Answering With Attention Priors](https://openaccess.thecvf.com/content/WACV2023/papers/Le_Guiding_Visual_Question_Answering_With_Attention_Priors_WACV_2023_paper.pdf)
* VideoQA
* [Dense but Efficient VideoQA for Intricate Compositional Reasoning](https://arxiv.org/abs/2210.10300)
* [Text-Guided Object Detector for Multi-modal Video Question Answering](https://openaccess.thecvf.com/content/WACV2023/papers/Shen_Text-Guided_Object_Detector_for_Multi-Modal_Video_Question_Answering_WACV_2023_paper.pdf)
* [Watching the News: Towards VideoQA Models that can Read](https://openaccess.thecvf.com/content/WACV2023/papers/Jahagirdar_Watching_the_News_Towards_VideoQA_Models_That_Can_Read_WACV_2023_paper.pdf)
:house:[project](http://cvit.iiit.ac.in/research/projects/cvit-projects/videoqa)
* 视觉问题生成
* [K-VQG: Knowledge-Aware Visual Question Generation for Common-Sense Acquisition](https://openaccess.thecvf.com/content/WACV2023/papers/Uehara_K-VQG_Knowledge-Aware_Visual_Question_Generation_for_Common-Sense_Acquisition_WACV_2023_paper.pdf)
:house:[project](https://uehara-mech.github.io/kvqg)
## 34.SLAM\Robots
* SLAM
* [Probabilistic Volumetric Fusion for Dense Monocular SLAM](https://arxiv.org/abs/2210.01276)
* AR
* [Heightfields for Efficient Scene Reconstruction for AR](https://openaccess.thecvf.com/content/WACV2023/papers/Watson_Heightfields_for_Efficient_Scene_Reconstruction_for_AR_WACV_2023_paper.pdf)
:star:[code](https://github.com/nianticlabs/heightfields)
## 33.View Synthesis(视图合成)
* [Vision Transformer for NeRF-Based View Synthesis From a Single Input Image](https://openaccess.thecvf.com/content/WACV2023/papers/Lin_Vision_Transformer_for_NeRF-Based_View_Synthesis_From_a_Single_Input_WACV_2023_paper.pdf)
* [Self-improving Multiplane-to-layer Images for Novel View Synthesis](https://openaccess.thecvf.com/content/WACV2023/papers/Solovev_Self-Improving_Multiplane-To-Layer_Images_for_Novel_View_Synthesis_WACV_2023_paper.pdf)
:house:[project](https://samsunglabs.github.io/MLI/)
## 32.Continual Learning(持续学习)
* [Continual Learning with Dependency Preserving Hypernetworks](https://arxiv.org/abs/2209.07712)
* [Do Pre-trained Models Benefit Equally in Continual Learning](https://arxiv.org/abs/2210.15701)
:star:[code](https://github.com/eric11220/pretrained-models-in-CL)
* [Saliency Guided Experience Packing for Replay in Continual Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Saha_Saliency_Guided_Experience_Packing_for_Replay_in_Continual_Learning_WACV_2023_paper.pdf)
## 31.Deepfake Detection(假象检测)
* [TI2Net: Temporal Identity Inconsistency Network for Deepfake Detection](https://openaccess.thecvf.com/content/WACV2023/papers/Liu_TI2Net_Temporal_Identity_Inconsistency_Network_for_Deepfake_Detection_WACV_2023_paper.pdf)
* 图像伪造
* [CFL-Net: Image Forgery Localization Using Contrastive Learning](https://arxiv.org/abs/2210.02182)
:star:[code](https://github.com/niloy193/CFLNet)
## 30.Reinforcement Learning(强化学习)
* [Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning](https://arxiv.org/abs/2212.03230)
:star:[code](https://github.com/ukyh/switchdisccaption)
## 29.Image Classification(图像分类)
* [Wavelength-Aware 2D Convolutions for Hyperspectral Imaging](https://openaccess.thecvf.com/content/WACV2023/papers/Varga_Wavelength-Aware_2D_Convolutions_for_Hyperspectral_Imaging_WACV_2023_paper.pdf)
:star:[code](https://github.com/cogsys-tuebingen/hyve_conv)
* [ML-Decoder: Scalable and Versatile Classification Head](https://openaccess.thecvf.com/content/WACV2023/papers/Ridnik_ML-Decoder_Scalable_and_Versatile_Classification_Head_WACV_2023_paper.pdf)
* [CNN2Graph: Building Graphs for Image Classification](https://openaccess.thecvf.com/content/WACV2023/papers/Trivedy_CNN2Graph_Building_Graphs_for_Image_Classification_WACV_2023_paper.pdf)
* [Token Pooling in Vision Transformers for Image Classification](https://openaccess.thecvf.com/content/WACV2023/papers/Marin_Token_Pooling_in_Vision_Transformers_for_Image_Classification_WACV_2023_paper.pdf)
* [Augmentation by Counterfactual Explanation -Fixing an Overconfident Classifier](https://openaccess.thecvf.com/content/WACV2023/papers/Singla_Augmentation_by_Counterfactual_Explanation_-_Fixing_an_Overconfident_Classifier_WACV_2023_paper.pdf)
* [Treatment Learning Causal Transformer for Noisy Image Classification](https://openaccess.thecvf.com/content/WACV2023/papers/Yang_Treatment_Learning_Causal_Transformer_for_Noisy_Image_Classification_WACV_2023_paper.pdf)
:star:[code](https://github.com/huckiyang/treatment-causal-transformer)
* 长尾识别
* [Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition](https://arxiv.org/abs/2209.02960)
* [Mutual Learning for Long-Tailed Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Park_Mutual_Learning_for_Long-Tailed_Recognition_WACV_2023_paper.pdf)
* pen-Set Classification
* [Large-Scale Open-Set Classification Protocols for ImageNet](https://arxiv.org/abs/2210.06789)
* 细粒度分类
* [SSFE-Net: Self-Supervised Feature Enhancement for Ultra-Fine-Grained Few-Shot Class Incremental Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Pan_SSFE-Net_Self-Supervised_Feature_Enhancement_for_Ultra-Fine-Grained_Few-Shot_Class_Incremental_Learning_WACV_2023_paper.pdf)
* 多标签分类
* [Spatial Consistency Loss for Training Multi-Label Classifiers From Single-Label Annotations](https://openaccess.thecvf.com/content/WACV2023/papers/Verelst_Spatial_Consistency_Loss_for_Training_Multi-Label_Classifiers_From_Single-Label_Annotations_WACV_2023_paper.pdf)
* 小样本分类
* [Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification](https://openaccess.thecvf.com/content/WACV2023/papers/Subramanyam_Contrastive_Knowledge-Augmented_Meta-Learning_for_Few-Shot_Classification_WACV_2023_paper.pdf)
:star:[code](https://github.com/Rakshith-2905/CAML)
* [CORL: Compositional Representation Learning for Few-Shot Classification](https://openaccess.thecvf.com/content/WACV2023/papers/He_CORL_Compositional_Representation_Learning_for_Few-Shot_Classification_WACV_2023_paper.pdf)
## 28.Pose Estimation(姿态估计)
* [COPE: End-to-end trainable Constant Runtime Object Pose Estimation](https://openaccess.thecvf.com/content/WACV2023/papers/Thalhammer_COPE_End-to-End_Trainable_Constant_Runtime_Object_Pose_Estimation_WACV_2023_paper.pdf)
* 6D
* [CRT-6D: Fast 6D Object Pose Estimation with Cascaded Refinement Transformers](https://arxiv.org/abs/2210.11718)
:star:[code](https://github.com/PedroCastro/CRT-6D)
* [SD-Pose: Structural Discrepancy Aware Category-Level 6D Object Pose Estimation](https://openaccess.thecvf.com/content/WACV2023/papers/Li_SD-Pose_Structural_Discrepancy_Aware_Category-Level_6D_Object_Pose_Estimation_WACV_2023_paper.pdf)
* 物体计数
* [Few-shot Object Counting with Similarity-Aware Feature Enhancement](https://openaccess.thecvf.com/content/WACV2023/papers/You_Few-Shot_Object_Counting_With_Similarity-Aware_Feature_Enhancement_WACV_2023_paper.pdf)
:star:[code](https://github.com/zhiyuanyou/SAFECount)
* 物体重识别
* [Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification](https://openaccess.thecvf.com/content/WACV2023/papers/Piano_Bent__Broken_Bicycles_Leveraging_Synthetic_Data_for_Damaged_Object_WACV_2023_paper.pdf)
:house:[project](https://tinyurl.com/37tepf7m)
## 27.Person ReID(人员重识别)
* 行人分析
* [Towards A Framework for Privacy-Preserving Pedestrian Analysis](https://openaccess.thecvf.com/content/WACV2023/papers/Kunchala_Towards_a_Framework_for_Privacy-Preserving_Pedestrian_Analysis_WACV_2023_paper.pdf)
* 行人搜索
* [Gallery Filter Network for Person Search](https://arxiv.org/abs/2210.12903)
:star:[code](https://github.com/LukeJaffe/GFN)
* [UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval](https://openaccess.thecvf.com/content/WACV2023/papers/Specker_UPAR_Unified_Pedestrian_Attribute_Recognition_and_Person_Retrieval_WACV_2023_paper.pdf)
:star:[code](https://github.com/speckean/upar_dataset)
* [SAT: Scale-Augmented Transformer for Person Search](https://openaccess.thecvf.com/content/WACV2023/papers/Fiaz_SAT_Scale-Augmented_Transformer_for_Person_Search_WACV_2023_paper.pdf)
* Re-id
* [Camera Alignment and Weighted Contrastive Learning for Domain Adaptation in Video Person ReID](https://arxiv.org/abs/2211.03626)
:star:[code](https://github.com/dmekhazni/CAWCL-ReID)
* [MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification](https://arxiv.org/abs/2211.04656)
:star:[code](https://github.com/Kitware/MEVID)
* [Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification](https://arxiv.org/abs/2212.09498)
* [Graph-Based Self-Learning for Robust Person Re-Identification](https://openaccess.thecvf.com/content/WACV2023/papers/Xian_Graph-Based_Self-Learning_for_Robust_Person_Re-Identification_WACV_2023_paper.pdf)
* [Body Part-Based Representation Learning for Occluded Person Re-Identification](https://openaccess.thecvf.com/content/WACV2023/papers/Somers_Body_Part-Based_Representation_Learning_for_Occluded_Person_Re-Identification_WACV_2023_paper.pdf)
:star:[code](https://github.com/VlSomers/bpbreid)
* 步态识别
* [Gait Recognition Using 3-D Human Body Shape Inference](https://arxiv.org/abs/2212.09042)
* 步态迁移
* [CTrGAN: Cycle Transformers GAN for Gait Transfer](https://openaccess.thecvf.com/content/WACV2023/papers/Mahpod_CTrGAN_Cycle_Transformers_GAN_for_Gait_Transfer_WACV_2023_paper.pdf)
* 嫌疑人识别
* [A Suspect Identification Framework using Contrastive Relevance Feedback](https://cdn.iiit.ac.in/cdn/precog.iiit.ac.in/pubs/WACV_2023_FaIRCoP_Camera_Ready.pdf)
* 人群计数
* [Dynamic Mixture of Counter Network for Location-Agnostic Crowd Counting](https://openaccess.thecvf.com/content/WACV2023/papers/Wang_Dynamic_Mixture_of_Counter_Network_for_Location-Agnostic_Crowd_Counting_WACV_2023_paper.pdf)
## 26.Dataset\Benchmark(数据集\基准)
* [OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping](https://arxiv.org/abs/2210.10732)
:sunflower:[dataset](https://open-earth-map.org/)
* [A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials](https://openaccess.thecvf.com/content/WACV2023/papers/Li_A_Continual_Deepfake_Detection_Benchmark_Dataset_Methods_and_Essentials_WACV_2023_paper.pdf)
:sunflower:[dataset](https://github.com/Coral79/CDDB)
* [The CropAndWeed Dataset: a Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation](https://openaccess.thecvf.com/content/WACV2023/papers/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.pdf)
:sunflower:[dataset](https://github.com/cropandweed/cropandweed-dataset)
* [IDD-3D: A Dataset for Driving in Unstructured Road Scenes](https://arxiv.org/abs/2210.12878)
:sunflower:[dataset](https://github.com/shubham1810/idd3d_kit)
* [Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation](https://openaccess.thecvf.com/content/WACV2023/papers/Soumm_Vis2Rec_A_Large-Scale_Visual_Dataset_for_Visit_Recommendation_WACV_2023_paper.pdf)
:sunflower:[dataset](https://github.com/MSoumm/Vis2Rec)
* [Creating a Forensic Database of Shoeprints from Online Shoe-Tread Photos](https://openaccess.thecvf.com/content/WACV2023/papers/Shafique_Creating_a_Forensic_Database_of_Shoeprints_From_Online_Shoe-Tread_Photos_WACV_2023_paper.pdf)
:sunflower:[dataset](https://github.com/Samia067/ShoeRinsics)
* 目标检测、分割、跟踪
* [BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video](https://openaccess.thecvf.com/content/WACV2023/papers/Athar_BURST_A_Benchmark_for_Unifying_Object_Recognition_Segmentation_and_Tracking_WACV_2023_paper.pdf)
:star:[code](https://github.com/Ali2500/BURST-benchmark)
## 25.Image Captioning(图像字幕)
* 人体图像分析
* [Split To Learn: Gradient Split for Multi-Task Human Image Analysis](https://openaccess.thecvf.com/content/WACV2023/papers/Deng_Split_To_Learn_Gradient_Split_for_Multi-Task_Human_Image_Analysis_WACV_2023_paper.pdf)
* 图像字幕
* [Expert-defined Keywords Improve Interpretability of Retinal Image Captioning](https://openaccess.thecvf.com/content/WACV2023/papers/Wu_Expert-Defined_Keywords_Improve_Interpretability_of_Retinal_Image_Captioning_WACV_2023_paper.pdf)
:star:[code](https://github.com/Jhhuangkay/Expert-defined-Keywords-Improve-Interpretability-of-Retinal-Image-Captioning)
* 视频字幕
* [Lightweight Video Denoising Using Aggregated Shifted Window Attention](https://openaccess.thecvf.com/content/WACV2023/papers/Lindner_Lightweight_Video_Denoising_Using_Aggregated_Shifted_Window_Attention_WACV_2023_paper.pdf)
:star:[code](https://github.com/LLindn/ASwin-Video-Denoising)
## 24.Image Retrieval(图像检索)
* [Boosting vision transformers for image retrieval](https://arxiv.org/abs/2210.11909)
:star:[code](https://github.com/dealicious-inc/DToP)
* [Certified Defense for Content Based Image Retrieval](https://openaccess.thecvf.com/content/WACV2023/papers/Kakizaki_Certified_Defense_for_Content_Based_Image_Retrieval_WACV_2023_paper.pdf)
* [Fashion Image Retrieval with Text Feedback by Additive Attention Compositional Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Tian_Fashion_Image_Retrieval_With_Text_Feedback_by_Additive_Attention_Compositional_WACV_2023_paper.pdf)
* [Content-Based Music-Image Retrieval Using Self- and Cross-Modal Feature Embedding Memory](https://openaccess.thecvf.com/content/WACV2023/papers/Nakatsuka_Content-Based_Music-Image_Retrieval_Using_Self-_and_Cross-Modal_Feature_Embedding_Memory_WACV_2023_paper.pdf)
* 图像-句子检索
* [Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval](https://arxiv.org/abs/2210.08908)
* 图像-文本检索
* [Dissecting Deep Metric Learning Losses for Image-Text Retrieval](https://arxiv.org/abs/2210.13188)
:star:[code](https://github.com/littleredxh/VSE-Gradient)
* [NAPReg: Nouns As Proxies Regularization for Semantically Aware Cross-Modal Embeddings](https://openaccess.thecvf.com/content/WACV2023/papers/Jawade_NAPReg_Nouns_As_Proxies_Regularization_for_Semantically_Aware_Cross-Modal_Embeddings_WACV_2023_paper.pdf)
:star:[code](https://github.com/bhavinjawade/NAPReq)
* 跨域检索
* [Contrastive Learning of Semantic Concepts for Open-set Cross-domain Retrieval](https://openaccess.thecvf.com/content/WACV2023/papers/Agarwal_Contrastive_Learning_of_Semantic_Concepts_for_Open-Set_Cross-Domain_Retrieval_WACV_2023_paper.pdf)
* 图像-文本匹配
* [More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching](https://openaccess.thecvf.com/content/WACV2023/papers/Chen_More_Than_Just_Attention_Improving_Cross-Modal_Attentions_With_Contrastive_Constraints_WACV_2023_paper.pdf)
## 23.Autonomous Driving(智能驾驶)
* [IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes](https://arxiv.org/abs/2210.12878)
:star:[code](https://github.com/shubham1810/idd3d_kit)
* [PP4AV: A Benchmarking Dataset for Privacy-Preserving Autonomous Driving](https://openaccess.thecvf.com/content/WACV2023/papers/Trinh_PP4AV_A_Benchmarking_Dataset_for_Privacy-Preserving_Autonomous_Driving_WACV_2023_paper.pdf)
:star:[code](https://github.com/khaclinh/pp4av)
* [Benchmarking Visual Localization for Autonomous Navigation](https://openaccess.thecvf.com/content/WACV2023/papers/Suomela_Benchmarking_Visual_Localization_for_Autonomous_Navigation_WACV_2023_paper.pdf)
:star:[code](https://github.com/lasuomela/carla_vloc_benchmark)
* 车辆重识别
* [Relation Preserving Triplet Mining for Stabilising the Triplet Loss In re-Identification Systems](https://openaccess.thecvf.com/content/WACV2023/papers/Ghosh_Relation_Preserving_Triplet_Mining_for_Stabilising_the_Triplet_Loss_In_WACV_2023_paper.pdf)
:star:[code](https://github.com/adhirajghosh/RPTM_reid)
* 车道线检测
* [Learning to Detect 3D Lanes by Shape Matching and Embedding](https://openaccess.thecvf.com/content/WACV2023/papers/Liu_Learning_To_Detect_3D_Lanes_by_Shape_Matching_and_Embedding_WACV_2023_paper.pdf)
* [3D-SpLineNet: 3D Traffic Line Detection using Parametric Spline Representations](https://openaccess.thecvf.com/content/WACV2023/papers/Pittner_3D-SpLineNet_3D_Traffic_Line_Detection_Using_Parametric_Spline_Representations_WACV_2023_paper.pdf)
:house:[project](https://3d-splinenet.github.io/)
* 轨迹预测
* [Robustness of Trajectory Prediction Models Under Map-Based Attacks](https://openaccess.thecvf.com/content/WACV2023/papers/Zheng_Robustness_of_Trajectory_Prediction_Models_Under_Map-Based_Attacks_WACV_2023_paper.pdf)
## 22.Human Action Recognition(人体动作识别与检测)
* 动作识别
* [Modality Mixer for Multi-modal Action Recognition](https://arxiv.org/abs/2208.11314)
* [STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition](https://arxiv.org/abs/2210.07503)
* [Holistic Interaction Transformer Network for Action Detection](https://arxiv.org/abs/2210.12686)
:star:[code](https://github.com/joslefaure/HIT)
* [Reconstructing Humpty Dumpty: Multi-feature Graph Autoencoder for Open Set Action Recognition](https://arxiv.org/abs/2212.06023)
:star:[code](https://github.com/Kitware/graphautoencoder)
* [DA-AIM: Exploiting Instance-based Mixed Sampling via Auxiliary Source Domain Supervision for Domain-adaptive Action Detection](https://arxiv.org/pdf/2209.15439.pdf)
:star:[code](https://github.com/wwwfan628/DA-AIM)
* [Spatio-Temporal Action Detection Under Large Motion](https://arxiv.org/pdf/2209.02250.pdf)
:star:[code](https://github.com/gurkirt/ActionTrackDetectron)
* [Efficient Skeleton-Based Action Recognition via Joint-Mapping Strategies](https://openaccess.thecvf.com/content/WACV2023/papers/Kang_Efficient_Skeleton-Based_Action_Recognition_via_Joint-Mapping_Strategies_WACV_2023_paper.pdf)
* [Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Broome_Recur_Attend_or_Convolve_On_Whether_Temporal_Modeling_Matters_for_WACV_2023_paper.pdf)
* [Semantics Guided Contrastive Learning of Transformers for Zero-Shot Temporal Activity Detection](https://openaccess.thecvf.com/content/WACV2023/papers/Nag_Semantics_Guided_Contrastive_Learning_of_Transformers_for_Zero-Shot_Temporal_Activity_WACV_2023_paper.pdf)
* [Adaptive Local-Component-Aware Graph Convolutional Network for One-Shot Skeleton-Based Action Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Zhu_Adaptive_Local-Component-Aware_Graph_Convolutional_Network_for_One-Shot_Skeleton-Based_Action_Recognition_WACV_2023_paper.pdf)
* [Multi-View Action Recognition using Contrastive Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Shah_Multi-View_Action_Recognition_Using_Contrastive_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/kshah33/ViewCon)
* [Stop or Forward: Dynamic Layer Skipping for Efficient Action Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Seon_Stop_or_Forward_Dynamic_Layer_Skipping_for_Efficient_Action_Recognition_WACV_2023_paper.pdf)
* [A Simple and Efficient Pipeline to Build an End-to-End Spatial-Temporal Action Detector](https://openaccess.thecvf.com/content/WACV2023/papers/Sui_A_Simple_and_Efficient_Pipeline_To_Build_an_End-to-End_Spatial-Temporal_WACV_2023_paper.pdf)
:star:[code](https://github.com/4paradigm-CV/SE-STAD)
* 时序动作定位
* [Temporal Feature Enhancement Dilated Convolution Network for Weakly-Supervised Temporal Action Localization](https://openaccess.thecvf.com/content/WACV2023/papers/Zhou_Temporal_Feature_Enhancement_Dilated_Convolution_Network_for_Weakly-Supervised_Temporal_Action_WACV_2023_paper.pdf)
* [Action-aware Masking Network with Group-based Attention for Temporal Action Localization](https://openaccess.thecvf.com/content/WACV2023/papers/Kang_Action-Aware_Masking_Network_With_Group-Based_Attention_for_Temporal_Action_Localization_WACV_2023_paper.pdf)
## 21.Point Cloud(点云)
* [PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds](https://arxiv.org/abs/2210.08305)
* [Visualizing Global Explanations of Point Cloud DNNs](https://openaccess.thecvf.com/content/WACV2023/papers/Tan_Visualizing_Global_Explanations_of_Point_Cloud_DNNs_WACV_2023_paper.pdf)
:star:[code](https://github.com/Explain3D/PointCloudAM)
* [RSF: Optimizing Rigid Scene Flow From 3D Point Clouds Without Labels](https://openaccess.thecvf.com/content/WACV2023/papers/Deng_RSF_Optimizing_Rigid_Scene_Flow_From_3D_Point_Clouds_Without_WACV_2023_paper.pdf)
* [Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis](https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Nearest_Neighbors_Meet_Deep_Neural_Networks_for_Point_Cloud_Analysis_WACV_2023_paper.pdf)
* [Explainability-Aware One Point Attack for Point Cloud Neural Networks](https://openaccess.thecvf.com/content/WACV2023/papers/Tan_Explainability-Aware_One_Point_Attack_for_Point_Cloud_Neural_Networks_WACV_2023_paper.pdf)
:star:[code](https://github.com/Explain3D/Exp-One-Point-Atk-PC)
* [Centroid Distance Keypoint Detector for Colored Point Clouds](https://openaccess.thecvf.com/content/WACV2023/papers/Teng_Centroid_Distance_Keypoint_Detector_for_Colored_Point_Clouds_WACV_2023_paper.pdf)
:star:[code](https://github.com/UCR-Robotics/CED_Detector)
* 点云分类
* [Fractual Projection Forest: Fast and Explainable Point Cloud Classifier](https://openaccess.thecvf.com/content/WACV2023/papers/Tan_Fractual_Projection_Forest_Fast_and_Explainable_Point_Cloud_Classifier_WACV_2023_paper.pdf)
:star:[code](https://github.com/Explain3D/FracProjForest)
* [Cross-Modality Feature Fusion Network for Few-Shot 3D Point Cloud Classification](https://openaccess.thecvf.com/content/WACV2023/papers/Yang_Cross-Modality_Feature_Fusion_Network_for_Few-Shot_3D_Point_Cloud_Classification_WACV_2023_paper.pdf)
* 点云分割
* [Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly](https://openaccess.thecvf.com/content/WACV2023/papers/Wu_Sim2real_Transfer_Learning_for_Point_Cloud_Segmentation_An_Industrial_Application_WACV_2023_paper.pdf)
* 点云语义分割
* [GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation](https://openaccess.thecvf.com/content/WACV2023/papers/Lee_GaIA_Graphical_Information_Gain_Based_Attention_Network_for_Weakly_Supervised_WACV_2023_paper.pdf)
* 点云配准
* [Overlap-guided Gaussian Mixture Models for Point Cloud Registration](https://arxiv.org/abs/2210.09836)
:star:[code](https://github.com/gfmei/ogmm)
* [SGPCR: Spherical Gaussian Point Cloud Representation and its Application to Object Registration and Retrieval](https://openaccess.thecvf.com/content/WACV2023/papers/Salihu_SGPCR_Spherical_Gaussian_Point_Cloud_Representation_and_Its_Application_To_WACV_2023_paper.pdf)
* 点云重建
* [PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors](https://arxiv.org/abs/2211.08702)
:star:[code](https://github.com/hkust-vgd/point_inverter)
* 3D点云
* [PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud](https://arxiv.org/abs/2211.15759)
* [Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors](https://openaccess.thecvf.com/content/WACV2023/papers/Bergmann_Anomaly_Detection_in_3D_Point_Clouds_Using_Deep_Geometric_Descriptors_WACV_2023_paper.pdf)
## 20.Transformer
* [EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification](https://arxiv.org/abs/2210.04615)
:star:[code](https://github.com/UARK-AICV/Embryos)
* [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://arxiv.org/abs/2210.12843)
:star:[code](https://github.com/lambert-x/Medical_MAE)
* [Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets](https://arxiv.org/abs/2210.12333)
:star:[code](https://github.com/xiangyu8/SATA)
* [Couplformer: Rethinking Vision Transformer With Coupling Attention](https://openaccess.thecvf.com/content/WACV2023/papers/Lan_Couplformer_Rethinking_Vision_Transformer_With_Coupling_Attention_WACV_2023_paper.pdf)
* [Trans4Map: Revisiting Holistic Bird's-Eye-View Mapping From Egocentric Images to Allocentric Semantics With Vision Transformers](https://openaccess.thecvf.com/content/WACV2023/papers/Chen_Trans4Map_Revisiting_Holistic_Birds-Eye-View_Mapping_From_Egocentric_Images_to_Allocentric_WACV_2023_paper.pdf)
:star:[code](https://github.com/jamycheung/Trans4Map)
* [PatchDropout: Economizing Vision Transformers Using Patch Dropout](https://openaccess.thecvf.com/content/WACV2023/papers/Liu_PatchDropout_Economizing_Vision_Transformers_Using_Patch_Dropout_WACV_2023_paper.pdf)
:star:[code](https://github.com/yueliukth/PatchDropout)
* [OutfitTransformer: Learning Outfit Representations for Fashion Recommendation](https://openaccess.thecvf.com/content/WACV2023/papers/Sarkar_OutfitTransformer_Learning_Outfit_Representations_for_Fashion_Recommendation_WACV_2023_paper.pdf)
* [Discrete Cosin TransFormer: Image Modeling From Frequency Domain](https://openaccess.thecvf.com/content/WACV2023/papers/Li_Discrete_Cosin_TransFormer_Image_Modeling_From_Frequency_Domain_WACV_2023_paper.pdf)
* [Orthogonal Transforms For Learning Invariant Representations In Equivariant Neural Networks](https://openaccess.thecvf.com/content/WACV2023/papers/Singh_Orthogonal_Transforms_for_Learning_Invariant_Representations_in_Equivariant_Neural_Networks_WACV_2023_paper.pdf)
## 19.Model Compression\Knowledge Distillation\Pruning(模型压缩\知识蒸馏\剪枝)
* 剪枝
* [Pushing the Efficiency Limit Using Structured Sparse Convolutions](https://arxiv.org/abs/2210.12818)
:star:[code](https://github.com/vkvermaa/SSC)
* [Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning](https://arxiv.org/abs/2212.10005)
* 知识蒸馏
* [Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks](https://arxiv.org/abs/2210.16103)
* [Understanding the Role of Mixup in Knowledge Distillation: \\An Empirical Study](https://arxiv.org/abs/2211.03946)
:star:[code](https://github.com/hchoi71/MIX-KD)
* [Understanding the Role of Mixup in Knowledge Distillation:An Empirical Study](https://openaccess.thecvf.com/content/WACV2023/papers/Choi_Understanding_the_Role_of_Mixup_in_Knowledge_Distillation_An_Empirical_WACV_2023_paper.pdf)
:star:[code](https://github.com/hchoi71/MIX-KD)
* [TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation](https://openaccess.thecvf.com/content/WACV2023/papers/Hu_TinyHD_Efficient_Video_Saliency_Prediction_With_Heterogeneous_Decoders_Using_Hierarchical_WACV_2023_paper.pdf)
* [Online Knowledge Distillation for Multi-task Learning]](https://openaccess.thecvf.com/content/WACV2023/papers/Jacob_Online_Knowledge_Distillation_for_Multi-Task_Learning_WACV_2023_paper.pdf)
* [Adversarial local distribution regularization for knowledge distillation](https://openaccess.thecvf.com/content/WACV2023/papers/Nguyen-Duc_Adversarial_Local_Distribution_Regularization_for_Knowledge_Distillation_WACV_2023_paper.pdf)
* 自我蒸馏
* [SSSD: Self-Supervised Self Distillation](https://openaccess.thecvf.com/content/WACV2023/papers/Chen_SSSD_Self-Supervised_Self_Distillation_WACV_2023_paper.pdf)
* DC
* [SD-Conv: Towards the Parameter-Efficiency of Dynamic Convolution](https://openaccess.thecvf.com/content/WACV2023/papers/He_SD-Conv_Towards_the_Parameter-Efficiency_of_Dynamic_Convolution_WACV_2023_paper.pdf)
* 量化
* [HyperBlock Floating Point: Generalised Quantization Scheme for Gradient and Inference Computation](https://openaccess.thecvf.com/content/WACV2023/papers/do_Nascimento_Hyperblock_Floating_Point_Generalised_Quantization_Scheme_for_Gradient_and_Inference_WACV_2023_paper.pdf)
* [SPIQ: Data-Free Per-Channel Static Input Quantization](https://openaccess.thecvf.com/content/WACV2023/papers/Yvinec_SPIQ_Data-Free_Per-Channel_Static_Input_Quantization_WACV_2023_paper.pdf)
* [On Quantizing Implicit Neural Representations](https://openaccess.thecvf.com/content/WACV2023/papers/Gordon_On_Quantizing_Implicit_Neural_Representations_WACV_2023_paper.pdf)
* [Hyperspherical Quantization: Toward Smaller and More Accurate Models](https://openaccess.thecvf.com/content/WACV2023/papers/Liu_Hyperspherical_Quantization_Toward_Smaller_and_More_Accurate_Models_WACV_2023_paper.pdf)
* 轻量级
* [Learning Lightweight Neural Networks via Channel-Split Recurrent Convolution](https://openaccess.thecvf.com/content/WACV2023/papers/Wu_Learning_Lightweight_Neural_Networks_via_Channel-Split_Recurrent_Convolution_WACV_2023_paper.pdf)
:star:[code](https://github.com/tuaxon/CSR)
## 18.NAS(神经架构搜索)
* [Revisiting Training-free NAS Metrics: An Efficient Training-based Method](https://arxiv.org/abs/2211.08666)
:star:[code](https://github.com/taoyang1122/Revisit_TrainingFree_NAS)
* [SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search](https://openaccess.thecvf.com/content/WACV2023/papers/Yu_SVD-NAS_Coupling_Low-Rank_Approximation_and_Neural_Architecture_Search_WACV_2023_paper.pdf)
:star:[code](https://github.com/Yu-Zhewen/SVD-NAS)
* [FreeREA: Training-Free Evolution-based Architecture Search](https://openaccess.thecvf.com/content/WACV2023/papers/Cavagnero_FreeREA_Training-Free_Evolution-Based_Architecture_Search_WACV_2023_paper.pdf)
:star:[code](https://github.com/NiccoloCavagnero/FreeREA)
* [Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search](https://openaccess.thecvf.com/content/WACV2023/papers/Vu_Toward_Edge-Efficient_Dense_Predictions_With_Synergistic_Multi-Task_Neural_Architecture_Search_WACV_2023_paper.pdf)
## 17.OCR(文本检测)
* [OCR-VQGAN: Taming Text-within-Image Generation](https://arxiv.org/abs/2210.11248)
:star:[code](https://github.com/joanrod/ocr-vqgan)
* [Efficient few-shot learning for pixel-precise handwritten document layout analysis](https://arxiv.org/abs/2210.15570)
* [D-Extract: Extracting Dimensional Attributes From Product Images](https://openaccess.thecvf.com/content/WACV2023/papers/Ghosh_D-Extract_Extracting_Dimensional_Attributes_From_Product_Images_WACV_2023_paper.pdf)
:star:[code](https://github.com/amazon-science/dimension-extraction-dataset)
* 文本识别
* [Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition](https://arxiv.org/abs/2209.00641)
* 表格检测
* [LayerDoc: Layer-Wise Extraction of Spatial Hierarchical Structure in Visually-Rich Documents](https://openaccess.thecvf.com/content/WACV2023/papers/Mathur_LayerDoc_Layer-Wise_Extraction_of_Spatial_Hierarchical_Structure_in_Visually-Rich_Documents_WACV_2023_paper.pdf)
* LOGO检测
* [Image-Text Pre-Training for Logo Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Hubenthal_Image-Text_Pre-Training_for_Logo_Recognition_WACV_2023_paper.pdf)
* 文档检测
* [One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text](https://openaccess.thecvf.com/content/WACV2023/papers/Java_One-Shot_Doc_Snippet_Detection_Powering_Search_in_Document_Beyond_Text_WACV_2023_paper.pdf)
* 文档理解
* [Multi-scale Cell-based Layout Representation for Document Understanding](https://openaccess.thecvf.com/content/WACV2023/papers/Shi_Multi-Scale_Cell-Based_Layout_Representation_for_Document_Understanding_WACV_2023_paper.pdf)
:star:[code](https://github.com/mijungkim-rakuten/multi-scale-cell-based)
* 文本擦除
* [Modeling Stroke Mask for End-to-End Text Erasing](https://openaccess.thecvf.com/content/WACV2023/papers/Du_Modeling_Stroke_Mask_for_End-to-End_Text_Erasing_WACV_2023_paper.pdf)
## 16.Super-Resolution(超分辨率)
* [Single Image Super-Resolution via a Dual Interactive Implicit Neural Network](https://arxiv.org/abs/2210.12593)
* [HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars](https://arxiv.org/abs/2203.14863)
* [Deep Model-Based Super-Resolution With Non-Uniform Blur](https://openaccess.thecvf.com/content/WACV2023/papers/Laroche_Deep_Model-Based_Super-Resolution_With_Non-Uniform_Blur_WACV_2023_paper.pdf)
:star:[code](https://github.com/claroche-r/DMBSR)
* [Kernel-Aware Burst Blind Super-Resolution](https://openaccess.thecvf.com/content/WACV2023/papers/Lian_Kernel-Aware_Burst_Blind_Super-Resolution_WACV_2023_paper.pdf)
* [Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution](https://openaccess.thecvf.com/content/WACV2023/papers/Yoo_Enriched_CNN-Transformer_Feature_Aggregation_Networks_for_Super-Resolution_WACV_2023_paper.pdf)
:star:[code](https://github.com/jinsuyoo/act)
* [Joint Video Rolling Shutter Correction and Super-Resolution](https://openaccess.thecvf.com/content/WACV2023/papers/Gupta_Joint_Video_Rolling_Shutter_Correction_and_Super-Resolution_WACV_2023_paper.pdf)
* 视频超分辨率
* [Cross-Resolution Flow Propagation for Foveated Video Super-Resolution](https://arxiv.org/abs/2212.13525)
:star:[code](https://github.com/eugenelet/CRFP)
* [Fast Online Video Super-Resolution With Deformable Attention Pyramid](https://openaccess.thecvf.com/content/WACV2023/papers/Fuoli_Fast_Online_Video_Super-Resolution_With_Deformable_Attention_Pyramid_WACV_2023_paper.pdf)
* [Efficient Reference-based Video Super-Resolution (ERVSR):Single Reference Image Is All You Need](https://openaccess.thecvf.com/content/WACV2023/papers/Kim_Efficient_Reference-Based_Video_Super-Resolution_ERVSR_Single_Reference_Image_Is_All_WACV_2023_paper.pdf)(https://github.com/haewonc/ERVSR)
## 15.Image Synthesis(图像合成)
* [One-Shot Synthesis of Images and Segmentation Masks](https://arxiv.org/abs/2209.07547)
:star:[code](https://github.com/boschresearch/one-shot-synthesis)
* [Style-Guided Inference of Transformer for High-resolution Image Synthesis](https://arxiv.org/abs/2210.05533)
* [Evaluating Generative Networks Using Gaussian Mixtures of Image Features](https://openaccess.thecvf.com/content/WACV2023/papers/Luzi_Evaluating_Generative_Networks_Using_Gaussian_Mixtures_of_Image_Features_WACV_2023_paper.pdf)
* [More Control for Free! Image Synthesis with Semantic Diffusion Guidance](https://openaccess.thecvf.com/content/WACV2023/papers/Liu_More_Control_for_Free_Image_Synthesis_With_Semantic_Diffusion_Guidance_WACV_2023_paper.pdf)
* 图像生成
* [Adaptively-Realistic Image Generation from Stroke and Sketch with Diffusion Model](https://arxiv.org/abs/2208.12675)
:star:[code](https://github.com/cyj407/DiSS):house:[project](https://cyj407.github.io/DiSS/)
* [Spatially Multi-Conditional Image Generation](https://openaccess.thecvf.com/content/WACV2023/papers/Popovic_Spatially_Multi-Conditional_Image_Generation_WACV_2023_paper.pdf)
:star:[code](https://github.com/96ritika/TLAM)
* 文本-图像合成
* [Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation](https://arxiv.org/abs/2211.07751)
:star:[code](https://github.com/openai/glide-text2im)
* 文字引导的图像操作
* [Interactive Image Manipulation with Complex Text Instructions](https://arxiv.org/abs/2211.15352)
## 14.Un\Self\Semi-Supervised Learning(无\自\半监督学习)
* 自监督
* [Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and Beyond](https://arxiv.org/abs/2208.14439)
:star:[code](https://github.com/WesleyHsieh0806/SS-PRL)
* [Global-Local Self-Distillation for Visual Representation Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Lebailly_Global-Local_Self-Distillation_for_Visual_Representation_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/tileb1/global-local-self-distillation)
* [Accelerating Self-Supervised Learning via Efficient Training Strategies](https://openaccess.thecvf.com/content/WACV2023/papers/Kocyigit_Accelerating_Self-Supervised_Learning_via_Efficient_Training_Strategies_WACV_2023_paper.pdf)
* [FUSSL: Fuzzy Uncertain Self Supervised Learning](https://arxiv.org/abs/2210.15818)
* [Self-Supervised Correspondence Estimation via Multiview Registration](https://arxiv.org/abs/2212.03236)
:house:[project](https://mbanani.github.io/syncmatch/)
* [Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning](https://arxiv.org/abs/2212.11187)
* [Self-Supervised Relative Pose With Homography Model-Fitting in the Loop](https://openaccess.thecvf.com/content/WACV2023/papers/Muller_Self-Supervised_Relative_Pose_With_Homography_Model-Fitting_in_the_Loop_WACV_2023_paper.pdf)
* [Self-Distilled Self-supervised Representation Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Jang_Self-Distilled_Self-Supervised_Representation_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/hagiss/SDSSL)
* [Multi-Level Contrastive Learning for Self-Supervised Vision Transformers](https://openaccess.thecvf.com/content/WACV2023/papers/Mo_Multi-Level_Contrastive_Learning_for_Self-Supervised_Vision_Transformers_WACV_2023_paper.pdf)
* [Self-Supervised Distilled Learning for Multi-modal Misinformation Identification](https://openaccess.thecvf.com/content/WACV2023/papers/Mu_Self-Supervised_Distilled_Learning_for_Multi-Modal_Misinformation_Identification_WACV_2023_paper.pdf)
* [An Embedding-Dynamic Approach to Self-Supervised Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Moon_An_Embedding-Dynamic_Approach_to_Self-Supervised_Learning_WACV_2023_paper.pdf)
* 半监督
* [Class-Level Confidence Based 3D Semi-Supervised Learning](https://arxiv.org/abs/2210.10138)
* [Dynamic Re-Weighting for Long-Tailed Semi-Supervised Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Peng_Dynamic_Re-Weighting_for_Long-Tailed_Semi-Supervised_Learning_WACV_2023_paper.pdf)
* [Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Lazarow_Unifying_Distribution_Alignment_as_a_Loss_for_Imbalanced_Semi-Supervised_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/google-research/crest)
* [Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth](https://openaccess.thecvf.com/content/WACV2023/papers/Bansal_Semantics-Depth-Symbiosis_Deeply_Coupled_Semi-Supervised_Learning_of_Semantics_and_Depth_WACV_2023_paper.pdf)
* [Semi-Supervised Learning for Sparsely-Labeled Sequential Data:Application to Healthcare Video Processing](https://openaccess.thecvf.com/content/WACV2023/papers/Dubost_Semi-Supervised_Learning_for_Sparsely-Labeled_Sequential_Data_Application_to_Healthcare_Video_WACV_2023_paper.pdf)
* 无监督
* [TTTFlow: Unsupervised Test-Time Training with Normalizing Flow](https://openaccess.thecvf.com/content/WACV2023/papers/Osowiechi_TTTFlow_Unsupervised_Test-Time_Training_With_Normalizing_Flow_WACV_2023_paper.pdf)
:star:[code](https://github.com/GustavoVargasHakim/TTTFlow)
## 13.Image Segmentation(图像分割)
* [Image Segmentation-based Unsupervised Multiple Objects Discovery](https://arxiv.org/abs/2212.10124)
* [WSNet: Towards An Effective Method for Wound Image Segmentation](https://openaccess.thecvf.com/content/WACV2023/papers/Oota_WSNet_Towards_an_Effective_Method_for_Wound_Image_Segmentation_WACV_2023_paper.pdf)
:star:[code](https://github.com/subbareddy248/WSNET)
* [Autoencoder-based background reconstruction and foreground segmentation with background noise estimation](https://openaccess.thecvf.com/content/WACV2023/papers/Sauvalle_Autoencoder-Based_Background_Reconstruction_and_Foreground_Segmentation_With_Background_Noise_Estimation_WACV_2023_paper.pdf)
* [Unsupervised multi-object segmentation using attention and soft-argmax](https://openaccess.thecvf.com/content/WACV2023/papers/Sauvalle_Unsupervised_Multi-Object_Segmentation_Using_Attention_and_Soft-Argmax_WACV_2023_paper.pdf)
* VOS
* [Unsupervised Video Object Segmentation via Prototype Memory Network](https://arxiv.org/abs/2209.03712)
* [Treating Motion as Option To Reduce Motion Dependency in Unsupervised Video Object Segmentation](https://openaccess.thecvf.com/content/WACV2023/papers/Cho_Treating_Motion_as_Option_To_Reduce_Motion_Dependency_in_Unsupervised_WACV_2023_paper.pdf)
:star:[code](https://github.com/suhwan-cho/TMO)
* [A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation](https://openaccess.thecvf.com/content/WACV2023/papers/Ponimatkin_A_Simple_and_Powerful_Global_Optimization_for_Unsupervised_Video_Object_WACV_2023_paper.pdf)
:house:[project](https://ponimatkin.github.io/ssl-vos/)
* VSS
* [Domain Adaptive Video Semantic Segmentation via Cross-Domain Moving Object Mixing](https://arxiv.org/abs/2211.02307)
* [Human-in-the-Loop Video Semantic Segmentation Auto-Annotation](https://openaccess.thecvf.com/content/WACV2023/papers/Qiao_Human-in-the-Loop_Video_Semantic_Segmentation_Auto-Annotation_WACV_2023_paper.pdf)
* 语义分割
* [Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation](https://arxiv.org/abs/2210.07207)
:star:[code](https://github.com/dfki-av/AWT-for-CISS)
* [Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation](https://arxiv.org/abs/2212.07890)
* [Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation](https://arxiv.org/abs/2212.07911)
* [LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation](https://arxiv.org/abs/2211.11870)
:star:[code](https://github.com/fy-vision/LoopDA)
* [Empirical Generalization Study: Unsupervised Domain Adaptation vs. Domain Generalization Methods for Semantic Segmentation in the Wild](https://openaccess.thecvf.com/content/WACV2023/papers/Piva_Empirical_Generalization_Study_Unsupervised_Domain_Adaptation_vs._Domain_Generalization_Methods_WACV_2023_paper.pdf)
* [Semantic Segmentation with Active Semi-Supervised Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Rangnekar_Semantic_Segmentation_With_Active_Semi-Supervised_Learning_WACV_2023_paper.pdf)
* [Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation](https://openaccess.thecvf.com/content/WACV2023/papers/Islam_Self-Supervised_Learning_With_Local_Contrastive_Loss_for_Detection_and_Semantic_WACV_2023_paper.pdf)
* [Semantic Segmentation of Degraded Images Using Layer-Wise Feature Adjustor](https://openaccess.thecvf.com/content/WACV2023/papers/Endo_Semantic_Segmentation_of_Degraded_Images_Using_Layer-Wise_Feature_Adjustor_WACV_2023_paper.pdf)
* [Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift](https://arxiv.org/abs/2210.06749)
:star:[code](https://github.com/sharat29ag/contextual_class)
* [Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation](https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Cooperative_Self-Training_for_Multi-Target_Adaptive_Semantic_Segmentation_WACV_2023_paper.pdf)
:star:[code](https://github.com/Mael-zys/CoaST)
* [Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions](https://openaccess.thecvf.com/content/WACV2023/papers/Bruggemann_Refign_Align_and_Refine_for_Adaptation_of_Semantic_Segmentation_to_WACV_2023_paper.pdf)
:star:[code](https://github.com/brdav/refign)
* [BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs](https://openaccess.thecvf.com/content/WACV2023/papers/Peng_BEVSegFormer_Birds_Eye_View_Semantic_Segmentation_From_Arbitrary_Camera_Rigs_WACV_2023_paper.pdf)
* [ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts](https://openaccess.thecvf.com/content/WACV2023/papers/Sacha_ProtoSeg_Interpretable_Semantic_Segmentation_With_Prototypical_Parts_WACV_2023_paper.pdf)
:star:[code](https://github.com/gmum/proto-segmentation)
* [Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation](https://openaccess.thecvf.com/content/WACV2023/papers/Zheng_Complementary_Bi-Directional_Feature_Compression_for_Indoor_360deg_Semantic_Segmentation_With_WACV_2023_paper.pdf)
* [Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep earning and Uncertainty Quantification](https://openaccess.thecvf.com/content/WACV2023/papers/Rottmann_Automated_Detection_of_Label_Errors_in_Semantic_Segmentation_Datasets_via_WACV_2023_paper.pdf)
* 弱监督语义分割
* ingle Stage Weakly Supervised Semantic Segmentation of Complex Scenes](https://openaccess.thecvf.com/content/WACV2023/papers/Akiva_Single_Stage_Weakly_Supervised_Semantic_Segmentation_of_Complex_Scenes_WACV_2023_paper.pdf)
* 半监督语义分割
* [Pruning-Guided Curriculum Learning for Semi-Supervised Semantic Segmentation](https://openaccess.thecvf.com/content/WACV2023/papers/Kong_Pruning-Guided_Curriculum_Learning_for_Semi-Supervised_Semantic_Segmentation_WACV_2023_paper.pdf)
* Multi-class part parsing
* [AFPSNet: Multi-Class Part Parsing Based on Scaled Attention and Feature Fusion](https://openaccess.thecvf.com/content/WACV2023/papers/Alsudays_AFPSNet_Multi-Class_Part_Parsing_Based_on_Scaled_Attention_and_Feature_WACV_2023_paper.pdf)
* BEV segmentation
* [X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation](https://arxiv.org/abs/2210.06778)
:star:[code](https://github.com/robot-learning-freiburg/PanopticBEV)
* 全景分割
* [MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation](https://arxiv.org/abs/2210.07577)
* [PRN: Panoptic Refinement Network](https://openaccess.thecvf.com/content/WACV2023/papers/Sun_PRN_Panoptic_Refinement_Network_WACV_2023_paper.pdf)
* [Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images](https://openaccess.thecvf.com/content/WACV2023/papers/de_Geus_Intra-Batch_Supervision_for_Panoptic_Segmentation_on_High-Resolution_Images_WACV_2023_paper.pdf)
:house:[project](https://ddegeus.github.io/intra-batch-supervision/)
* 实例分割
* [From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments](https://arxiv.org/abs/2211.16200)
* [CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation](https://arxiv.org/abs/2212.14121)
* [Weakly Supervised Cell-Instance Segmentation With Two Types of Weak Labels by Single Instance Pasting](https://openaccess.thecvf.com/content/WACV2023/papers/Nishimura_Weakly_Supervised_Cell-Instance_Segmentation_With_Two_Types_of_Weak_Labels_WACV_2023_paper.pdf)
* [Self-Supervised Learning With Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic Radiographs](https://openaccess.thecvf.com/content/WACV2023/papers/Almalki_Self-Supervised_Learning_With_Masked_Image_Modeling_for_Teeth_Numbering_Detection_WACV_2023_paper.pdf)
:star:[code](https://github.com/AmaniHAlmalki/DentalMIM)
* [Weakly-Supervised Point Cloud Instance Segmentation With Geometric Priors](https://openaccess.thecvf.com/content/WACV2023/papers/Du_Weakly-Supervised_Point_Cloud_Instance_Segmentation_With_Geometric_Priors_WACV_2023_paper.pdf)
* [NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds](https://openaccess.thecvf.com/content/WACV2023/papers/Sun_NeuralBF_Neural_Bilateral_Filtering_for_Top-Down_Instance_Segmentation_on_Point_WACV_2023_paper.pdf)
:house:[project](https://neuralbf.github.io/)
* [SCTS: Instance Segmentation of Single Cells Using a Transformer-Based Semantic-Aware Model and Space-Filling Augmentation](https://openaccess.thecvf.com/content/WACV2023/papers/Zhou_SCTS_Instance_Segmentation_of_Single_Cells_Using_a_Transformer-Based_Semantic-Aware_WACV_2023_paper.pdf)
:star:[code](https://github.com/cbmi-group/SCTS)
* 小样本分割
* [Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation](https://arxiv.org/abs/2211.02300)
* [Learning Few-shot Segmentation from Bounding Box Annotations](https://openaccess.thecvf.com/content/WACV2023/papers/Han_Learning_Few-Shot_Segmentation_From_Bounding_Box_Annotations_WACV_2023_paper.pdf)
* 叶子疾病分割
* [AnoLeaf: Unsupervised Leaf Disease Segmentation via Structurally Robust Generative Inpainting](https://openaccess.thecvf.com/content/WACV2023/papers/Bhugra_AnoLeaf_Unsupervised_Leaf_Disease_Segmentation_via_Structurally_Robust_Generative_Inpainting_WACV_2023_paper.pdf)
* 细胞分割
* [Knowing What to Label for Few Shot Microscopy Image Cell Segmentation](https://arxiv.org/abs/2211.10244)
:star:[code](https://github.com/Yussef93/KnowWhatToLabel/)
* 目标分割
* [Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series](https://arxiv.org/abs/2212.14750)
* 抠图
* [Video Object Matting via Hierarchical Space-Time Semantic Guidance](https://openaccess.thecvf.com/content/WACV2023/papers/Wang_Video_Object_Matting_via_Hierarchical_Space-Time_Semantic_Guidance_WACV_2023_paper.pdf)
## 12.One\Few-Shot Learning or Domain Adaptation\Generalization\Shift(单\小样本学习 or 域适应\泛化\偏移)
* 域适应
* [Self-Distillation for Unsupervised 3D Domain Adaptation](https://arxiv.org/abs/2210.08226)
:house:[project](https://cvlab-unibo.github.io/FeatureDistillation/)
* [CoNMix for Source-free Single and Multi-target Domain Adaptation](https://arxiv.org/abs/2211.03876)
:star:[code](https://github.com/vcl-iisc/CoNMix):house:[project](https://sites.google.com/view/conmix-vcl)
* [Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation](https://arxiv.org/abs/2212.08355)
* [Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation](https://openaccess.thecvf.com/content/WACV2023/papers/Sahoo_Select_Label_and_Mix_Learning_Discriminative_Invariant_Feature_Representations_for_WACV_2023_paper.pdf)
:house:[project](https://cvir.github.io/projects/slm)
* [TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation](https://openaccess.thecvf.com/content/WACV2023/papers/Yang_TVT_Transferable_Vision_Transformer_for_Unsupervised_Domain_Adaptation_WACV_2023_paper.pdf)
:star:[code](https://github.com/uta-smile/TVT)
* [Backprop Induced Feature Weighting for Adversarial Domain Adaptation with Iterative Label Distribution Alignment](https://openaccess.thecvf.com/content/WACV2023/papers/Westfechtel_Backprop_Induced_Feature_Weighting_for_Adversarial_Domain_Adaptation_With_Iterative_WACV_2023_paper.pdf)
* [Generative Alignment of Posterior Probabilities for Source-free Domain Adaptation](https://openaccess.thecvf.com/content/WACV2023/papers/Chhabra_Generative_Alignment_of_Posterior_Probabilities_for_Source-Free_Domain_Adaptation_WACV_2023_paper.pdf)
* [SALAD : Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection](https://openaccess.thecvf.com/content/WACV2023/papers/Kothandaraman_SALAD_Source-Free_Active_Label-Agnostic_Domain_Adaptation_for_Classification_Segmentation_and_WACV_2023_paper.pdf)
* 半监督域适应
* [Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Rahman_Semi-Supervised_Domain_Adaptation_With_Auto-Encoder_via_Simultaneous_Learning_WACV_2023_paper.pdf)
* 域泛化
* [Intra-Source Style Augmentation for Improved Domain Generalization](https://arxiv.org/abs/2210.10175)
* [Center-aware Adversarial Augmentation for Single Domain Generalization](https://openaccess.thecvf.com/content/WACV2023/papers/Chen_Center-Aware_Adversarial_Augmentation_for_Single_Domain_Generalization_WACV_2023_paper.pdf)
* [FFM: Injecting Out-of-Domain Knowledge via Factorized Frequency Modification](https://openaccess.thecvf.com/content/WACV2023/papers/Wang_FFM_Injecting_Out-of-Domain_Knowledge_via_Factorized_Frequency_Modification_WACV_2023_paper.pdf)
* [Improving Diversity with Adversarially Learned Transformations for Domain Generalization](https://openaccess.thecvf.com/content/WACV2023/papers/Gokhale_Improving_Diversity_With_Adversarially_Learned_Transformations_for_Domain_Generalization_WACV_2023_paper.pdf)
:star:[code](https://github.com/tejas-gokhale/ALT)
* 零样本
* [Learning Attention Propagation for Compositional Zero-Shot Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Khan_Learning_Attention_Propagation_for_Compositional_Zero-Shot_Learning_WACV_2023_paper.pdf)
* 小样本
* [Aggregating Bilateral Attention for Few-Shot Instance Localization](https://openaccess.thecvf.com/content/WACV2023/papers/Hsieh_Aggregating_Bilateral_Attention_for_Few-Shot_Instance_Localization_WACV_2023_paper.pdf)
* [HyperShot: Few-Shot Learning by Kernel HyperNetworks](https://openaccess.thecvf.com/content/WACV2023/papers/Sendera_HyperShot_Few-Shot_Learning_by_Kernel_HyperNetworks_WACV_2023_paper.pdf)
:star:[code](https://github.com/gmum/few-shot-hypernets-public)
* [Few-Shot Learning of Compact Models via Task-Specific Meta Distillation](https://openaccess.thecvf.com/content/WACV2023/papers/Wu_Few-Shot_Learning_of_Compact_Models_via_Task-Specific_Meta_Distillation_WACV_2023_paper.pdf)
* [Semantic Guided Latent Parts Embedding for Few-Shot Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Yang_Semantic_Guided_Latent_Parts_Embedding_for_Few-Shot_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/MartaYang/LPE)
* [Self-Attention Message Passing for Contrastive Few-Shot Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Shirekar_Self-Attention_Message_Passing_for_Contrastive_Few-Shot_Learning_WACV_2023_paper.pdf)
:star:[code](https://github.com/ojss/SAMPTransfer/)
## 11.Face(人脸)
* [My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization](https://arxiv.org/abs/2211.01361)
* [Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles](https://openaccess.thecvf.com/content/WACV2023/papers/Hustinx_Improving_Deep_Facial_Phenotyping_for_Ultra-Rare_Disorder_Verification_Using_Model_WACV_2023_paper.pdf)
:star:[code](https://github.com/igsb/GestaltMatcher-Arc)
* 读唇术
* [Towards MOOCs for Lip Reading: Using Synthetic Talking Heads to Train Humans in Lipreading at Scale](https://arxiv.org/abs/2208.09796)
* 3D人脸
* [Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance](https://openaccess.thecvf.com/content/WACV2023/papers/Taherkhani_Controllable_3D_Generative_Adversarial_Face_Model_via_Disentangling_Shape_and_WACV_2023_paper.pdf)
:house:[project](https://aashishrai3799.github.io/3DFaceCAM/)
* [3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling](https://openaccess.thecvf.com/content/WACV2023/papers/Galanakis_3DMM-RF_Convolutional_Radiance_Fields_for_3D_Face_Modeling_WACV_2023_paper.pdf)
* 人脸识别
* [DigiFace-1M: 1 Million Digital Face Images for Face Recognition](https://arxiv.org/abs/2210.02579)
:star:[code](https://github.com/microsoft/DigiFace1M)
* [CAST: Conditional Attribute Subsampling Toolkit for Fine-Grained Evaluation](https://openaccess.thecvf.com/content/WACV2023/papers/Robbins_CAST_Conditional_Attribute_Subsampling_Toolkit_for_Fine-Grained_Evaluation_WACV_2023_paper.pdf)
:star:[code](https://github.com/WesRobbins/CAST)
* [CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning-Based Synthetic Face Detection](https://openaccess.thecvf.com/content/WACV2023/papers/Boyd_CYBORG_Blending_Human_Saliency_Into_the_Loss_Improves_Deep_Learning-Based_WACV_2023_paper.pdf)
* [Unifying Margin-Based Softmax Losses in Face Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Unifying_Margin-Based_Softmax_Losses_in_Face_Recognition_WACV_2023_paper.pdf)
* [Harnessing Unrecognizable Faces for Improving Face Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Deng_Harnessing_Unrecognizable_Faces_for_Improving_Face_Recognition_WACV_2023_paper.pdf)
* [QMagFace: Simple and Accurate Quality-Aware Face Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Terhorst_QMagFace_Simple_and_Accurate_Quality-Aware_Face_Recognition_WACV_2023_paper.pdf)
:star:[code](https://github.com/pterhoer/QMagFace)
* [A Quality Aware Sample-to-Sample Comparison for Face Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Saadabadi_A_Quality_Aware_Sample-to-Sample_Comparison_for_Face_Recognition_WACV_2023_paper.pdf)
* 人脸修复/恢复
* [Nested Deformable Multi-head Attention for Facial Image Inpainting](https://openaccess.thecvf.com/content/WACV2023/papers/Phutke_Nested_Deformable_Multi-Head_Attention_for_Facial_Image_Inpainting_WACV_2023_paper.pdf)
:star:[code](https://github.com/shrutiphutke/NDMA_Facial_Inpainting)
* [AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2208.11284)
* 人脸交换
* [FaceOff: A Video-to-Video Face Swapping System](https://arxiv.org/abs/2208.09788)
* [FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping](https://openaccess.thecvf.com/content/WACV2023/papers/Rosberg_FaceDancer_Pose-_and_Occlusion-Aware_High_Fidelity_Face_Swapping_WACV_2023_paper.pdf)
* [FastSwap: A Lightweight One-Stage Framework for Real-Time Face Swapping](https://openaccess.thecvf.com/content/WACV2023/papers/Yoo_FastSwap_A_Lightweight_One-Stage_Framework_for_Real-Time_Face_Swapping_WACV_2023_paper.pdf)
:star:[code](https://github.com/sahngmin/fastswap)
* 人脸表情识别
* [Uncertainty-aware Label Distribution Learning for Facial Expression Recognition](https://arxiv.org/abs/2209.10448)
:star:[code](https://github.com/minhnhatvt/label-distribution-learning-fer-tf)
* 微表情识别
* [RNAS-MER: A Refined Neural Architecture Search With Hybrid Spatiotemporal Operations for Micro-Expression Recognition](https://openaccess.thecvf.com/content/WACV2023/papers/Verma_RNAS-MER_A_Refined_Neural_Architecture_Search_With_Hybrid_Spatiotemporal_Operations_WACV_2023_paper.pdf)
* 人脸重现
* [Audio-Visual Face Reenactment](https://arxiv.org/abs/2210.02755)
:house:[project](http://cvit.iiit.ac.in/research/projects/cvit-projects/avfr)
* 人脸命名
* [Weakly Supervised Face Naming with Symmetry-Enhanced Contrastive Loss](https://arxiv.org/abs/2210.08957)
* 人脸重建
* [ReEnFP: Detail-Preserving Face Reconstruction by Encoding Facial Priors](https://openaccess.thecvf.com/content/WACV2023/papers/Sun_ReEnFP_Detail-Preserving_Face_Reconstruction_by_Encoding_Facial_Priors_WACV_2023_paper.pdf)
* 人脸合成
* [Scaling Neural Face Synthesis to High FPS and Low Latency by Neural Caching](https://openaccess.thecvf.com/content/WACV2023/papers/Yu_Scaling_Neural_Face_Synthesis_to_High_FPS_and_Low_Latency_WACV_2023_paper.pdf)
* [CG-NeRF: Conditional Generative Neural Radiance Fields for 3D-aware Image Synthesis](https://openaccess.thecvf.com/content/WACV2023/papers/Jo_CG-NeRF_Conditional_Generative_Neural_Radiance_Fields_for_3D-Aware_Image_Synthesis_WACV_2023_paper.pdf)
* Deepfake
* [Proactive Deepfake Defence via Identity Watermarking](https://openaccess.thecvf.com/content/WACV2023/papers/Zhao_Proactive_Deepfake_Defence_via_Identity_Watermarking_WACV_2023_paper.pdf)
* Facial Action Unit Detection
* [FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection](https://arxiv.org/abs/2211.06143)
* 人脸质量评估
* [IFQA: Interpretable Face Quality Assessment](https://arxiv.org/abs/2211.07077)
:star:[code](https://github.com/VCLLab/IFQA)
* 活体检测
* [Domain Invariant Vision Transformer Learning for Face Anti-Spoofing](https://openaccess.thecvf.com/content/WACV2023/papers/Liao_Domain_Invariant_Vision_Transformer_Learning_for_Face_Anti-Spoofing_WACV_2023_paper.pdf)
* 基于表情的脸部皱纹合成
* [Mesh-Tension Driven Expression-Based Wrinkles for Synthetic Faces](https://arxiv.org/abs/2210.03529)
* 文字和图像引导的3D头像生成
* [Text and Image Guided 3D Avatar Generation and Manipulation](https://openaccess.thecvf.com/content/WACV2023/papers/Canfes_Text_and_Image_Guided_3D_Avatar_Generation_and_Manipulation_WACV_2023_paper.pdf)
* 说话人脸
* [Towards Generating Ultra-High Resolution Talking-Face Videos with Lip synchronization](https://openaccess.thecvf.com/content/WACV2023/papers/Gupta_Towards_Generating_Ultra-High_Resolution_Talking-Face_Videos_With_Lip_Synchronization_WACV_2023_paper.pdf)
* 唇语阅读
* [Towards MOOCs for Lipreading: Using Synthetic Talking Heads to Train Humans in Lipreading at Scale](https://openaccess.thecvf.com/content/WACV2023/papers/Agarwal_Towards_MOOCs_for_Lipreading_Using_Synthetic_Talking_Heads_To_Train_WACV_2023_paper.pdf)
## 10.Adversarial Learning(对抗学习)
* [Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks](https://arxiv.org/abs/2209.09883)
* [Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training](https://openaccess.thecvf.com/content/WACV2023/papers/Lee_Inducing_Data_Amplification_Using_Auxiliary_Datasets_in_Adversarial_Training_WACV_2023_paper.pdf)
:star:[code](https://github.com/Saehyung-Lee/BiaMAT)
* [Interpreting Disparate Privacy-Utility Tradeoff in Adversarial Learning via Attribute Correlation](https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Interpreting_Disparate_Privacy-Utility_Tradeoff_in_Adversarial_Learning_via_Attribute_Correlation_WACV_2023_paper.pdf)
* [FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable Trade-off between Accuracy and Robustness](https://openaccess.thecvf.com/content/WACV2023/papers/Kundu_FLOAT_Fast_Learnable_Once-for-All_Adversarial_Training_for_Tunable_Trade-Off_Between_WACV_2023_paper.pdf)
* [Adversarial robustness in discontinuous spaces via alternating sampling & descent](https://openaccess.thecvf.com/content/WACV2023/papers/Venkatesh_Adversarial_Robustness_in_Discontinuous_Spaces_via_Alternating_Sampling__Descent_WACV_2023_paper.pdf)
* [PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch](https://openaccess.thecvf.com/content/WACV2023/papers/Xu_PatchZero_Defending_Against_Adversarial_Patch_Attacks_by_Detecting_and_Zeroing_WACV_2023_paper.pdf)
* [Avoiding Lingering in Learning Active Recognition by Adversarial Disturbance](https://openaccess.thecvf.com/content/WACV2023/papers/Fan_Avoiding_Lingering_in_Learning_Active_Recognition_by_Adversarial_Disturbance_WACV_2023_paper.pdf)
* 对抗样本
* [Closer Look at the Transferability of Adversarial Examples:How They Fool Different Models Differently](https://openaccess.thecvf.com/content/WACV2023/papers/Waseda_Closer_Look_at_the_Transferability_of_Adversarial_Examples_How_They_WACV_2023_paper.pdf)
* 主动攻击
* [Do Adaptive Active Attacks Pose Greater Risk Than Static Attacks?](https://openaccess.thecvf.com/content/WACV2023/papers/Drenkow_Do_Adaptive_Active_Attacks_Pose_Greater_Risk_Than_Static_Attacks_WACV_2023_paper.pdf)
## 9.Remote Sensing\Satellite Image(遥感\卫星图像)
* RS
* [Handling Image and Label Resolution Mismatch in Remote Sensing](https://arxiv.org/abs/2211.15790)
* [GAF-Net: Improving the Performance of Remote Sensing Image Fusion Using Novel Global Self and Cross Attention Learning](https://openaccess.thecvf.com/content/WACV2023/papers/Jha_GAF-Net_Improving_the_Performance_of_Remote_Sensing_Image_Fusion_Using_WACV_2023_paper.pdf)
* 变化检测
* [Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery](https://arxiv.org/abs/2212.10236)
:star:[code](https://github.com/seominseok0429/Self-Pair-for-Change-Detection)
* 航空图像检测
* [ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection](https://openaccess.thecvf.com/content/WACV2023/papers/Sairam_ARUBA_An_Architecture-Agnostic_Balanced_Loss_for_Aerial_Object_Detection_WACV_2023_paper.pdf)
* [Transformers For Recognition In Overhead Imagery: A Reality Check](https://openaccess.thecvf.com/content/WACV2023/papers/Luzi_Transformers_for_Recognition_in_Overhead_Imagery_A_Reality_Check_WACV_2023_paper.pdf)
* 航空图像分割
* [Semantic Segmentation in Aerial Imagery Using Multi-level Contrastive Learning with Local Consistency](https://openaccess.thecvf.com/content/WACV2023/papers/Tang_Semantic_Segmentation_in_Aerial_Imagery_Using_Multi-Level_Contrastive_Learning_With_WACV_2023_paper.pdf)
* 国际边界检测
* [Computer Vision for International Border Legibility](https://openaccess.thecvf.com/content/WACV2023/papers/Ortega_Computer_Vision_for_International_Border_Legibility_WACV_2023_paper.pdf)
## 8.Image Processing(图像处理)
* 图像质量评估
* [No Reference Opinion Unaware Quality Assessment of Authentically Distorted Images](https://openaccess.thecvf.com/content/WACV2023/papers/Babu_No_Reference_Opinion_Unaware_Quality_Assessment_of_Authentically_Distorted_Images_WACV_2023_paper.pdf)
:star:[code](https://github.com/nithincbabu7/iqa-ContentSep)
* 图像恢复
* [Large-to-small Image Resolution Asymmetry in Deep Metric Learning](https://arxiv.org/abs/2210.05463)
:star:[code](https://github.com/pavelsuma/raml)
* [DSTrans: Dual-Stream Transformer for Hyperspectral Image Restoration](https://openaccess.thecvf.com/content/WACV2023/papers/Yu_DSTrans_Dual-Stream_Transformer_for_Hyperspectral_Image_Restoration_WACV_2023_paper.pdf)
:star:[code](https://github.com/yudadabing/Dual-Stream-Transformer-for-Hyperspectral-Image-Restoration)
* [Semi-Supervised Learning for Low-light Image Restoration through Quality Assisted Pseudo-Labeling](https://openaccess.thecvf.com/content/WACV2023/papers/Malik_Semi-Supervised_Learning_for_Low-Light_Image_Restoration_Through_Quality_Assisted_Pseudo-Labeling_WACV_2023_paper.pdf)
:star:[code](https://github.com/sameerIISc/SSL-LLR)
* [Real-Time Restoration of Dark Stereo Images](https://openaccess.thecvf.com/content/WACV2023/papers/Lamba_Real-Time_Restoration_of_Dark_Stereo_Images_WACV_2023_paper.pdf)
:house:[project](https://mohitlamba94.github.io/darkstereo/)
* 图像修复
* [GeoFill: Reference-Based Image Inpainting With Better Geometric Understanding](https://openaccess.thecvf.com/content/WACV2023/papers/Zhao_GeoFill_Reference-Based_Image_Inpainting_With_Better_Geometric_Understanding_WACV_2023_paper.pdf)
* [Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand](https://openaccess.thecvf.com/content/WACV2023/papers/Jain_Keys_To_Better_Image_Inpainting_Structure_and_Texture_Go_Hand_WACV_2023_paper.pdf)
:house:[project](https://praeclarumjj3.github.io/fcf-inpainting/)
* 图像增强
* [Perceptual Image Enhancement for Smartphone Real-Time Applications](https://arxiv.org/abs/2210.13552)
:star:[code](https://github.com/mv-lab/AISP)
* [Robust Real-World Image Enhancement Based on Multi-Exposure LDR Images](https://openaccess.thecvf.com/content/WACV2023/papers/Ren_Robust_Real-World_Image_Enhancement_Based_on_Multi-Exposure_LDR_Images_WACV_2023_paper.pdf)
* [End-to-End Single-Frame Image Signal Processing for High Dynamic Range Scenes](https://openaccess.thecvf.com/content/WACV2023/papers/Dinh_End-to-End_Single-Frame_Image_Signal_Processing_for_High_Dynamic_Range_Scenes_WACV_2023_paper.pdf)
* [PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement](https://openaccess.thecvf.com/content/WACV2023/papers/Nguyen_PSENet_Progressive_Self-Enhancement_Network_for_Unsupervised_Extreme-Light_Image_Enhancement_WACV_2023_paper.pdf)
:star:[code](https://github.com/VinAIResearch/PSENet-Image-Enhancement)