https://github.com/52cv/wacv-2022-papers
https://github.com/52cv/wacv-2022-papers
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- Host: GitHub
- URL: https://github.com/52cv/wacv-2022-papers
- Owner: 52CV
- Created: 2022-01-06T03:36:37.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-04-11T05:50:14.000Z (about 4 years ago)
- Last Synced: 2025-02-24T05:14:35.340Z (over 1 year ago)
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- Stars: 38
- Watchers: 2
- Forks: 11
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 52CV-WACV-Papers
## 历年综述论文分类汇总戳这里↘️[CV-Surveys](https://github.com/52CV/CV-Surveys)施工中~~~~~~~~~~
## 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)
## 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 2022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 406 篇。
# :exclamation::exclamation::exclamation::star2::star2::star2:分类完成
# 目录
|:dog:|:mouse:|:hamster:|:tiger:|
|---------|---------|---------|---------|
|[53.Gaze Estimation(视线估计)](#53)|[54.Optical Flow(光流)](#54)|[55.Object Counting(物体计数)](#55)|
|[49.Debiasing(去偏见)](#49)|[50.Sign Language Translation(手语翻译)](#50)|[51.SSC(语义场景完成)](#51)|[52.Eye Tracking(眼动跟踪)](#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.Defect Detection(缺陷检测)](#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)|
## 55.Object Counting(物体计数)
* [Single Image Object Counting and Localizing Using Active-Learning](https://openaccess.thecvf.com/content/WACV2022/papers/Huberman-Spiegelglas_Single_Image_Object_Counting_and_Localizing_Using_Active-Learning_WACV_2022_paper.pdf)
## 54.Optical Flow(光流)
* [Detail Preserving Residual Feature Pyramid Modules for Optical Flow](https://arxiv.org/abs/2107.10990)
## 53.Gaze Estimation(视线估计)
* [MTGLS: Multi-Task Gaze Estimation With Limited Supervision](https://arxiv.org/abs/2110.12100)
## 52.Eye Tracking(眼动跟踪)
* [Event-Based Kilohertz Eye Tracking Using Coded Differential Lighting](https://openaccess.thecvf.com/content/WACV2022/papers/Stoffregen_Event-Based_Kilohertz_Eye_Tracking_Using_Coded_Differential_Lighting_WACV_2022_paper.pdf)
## 51.Semantic Scene Completion(语义场景完成SSC)
* [Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priors](https://arxiv.org/abs/2111.13309)
## 50.Sign Language Translation(手语翻译)
* [Sign Language Translation With Hierarchical Spatio-Temporal Graph Neural Network](https://openaccess.thecvf.com/content/WACV2022/papers/Kan_Sign_Language_Translation_With_Hierarchical_Spatio-Temporal_Graph_Neural_Network_WACV_2022_paper.pdf)
## 49.Debiasing(去偏见)
* [An Investigation of Critical Issues in Bias Mitigation Techniques](https://arxiv.org/abs/2104.00170)
:star:[code](https://github.com/erobic/bias-mitigators)
## 48.Light Fields(光场)
* [Fast and Efficient Restoration of Extremely Dark Light Fields](https://openaccess.thecvf.com/content/WACV2022/papers/Lamba_Fast_and_Efficient_Restoration_of_Extremely_Dark_Light_Fields_WACV_2022_paper.pdf)
* 相机校准
* [Modeling dynamic target deformation in camera calibration](https://arxiv.org/abs/2110.07322)
* Camera Pose Estimation(相机姿势估计)
* [A Structure-Aware Method for Direct Pose Estimation](https://arxiv.org/abs/2012.12360)
:star:[code](https://github.com/mvrl/structure-aware-pose-estimation)
## 47.Data Augmentation(数据增强)
* [Meta Approach to Data Augmentation Optimization](https://arxiv.org/abs/2006.07965)
* [Improving Model Generalization by Agreement of Learned Representations From Data Augmentation](https://arxiv.org/abs/2110.10536)
:star:[code](https://github.com/roatienza/agmax)
## 46.Metric Learning(度量学习)
* [Multi-Head Deep Metric Learning Using Global and Local Representations](https://arxiv.org/abs/2112.14327)
* [Hierarchical Proxy-Based Loss for Deep Metric Learning](https://arxiv.org/abs/2103.13538)
## 45.Class-Incremental Learning(类增量学习)
* [Dataset Knowledge Transfer for Class-Incremental Learning without Memory](https://arxiv.org/abs/2110.08421)
:star:[code](https://github.com/HabibSlim/DKT-for-CIL)
## 44.Multi-Task Learning(多任务学习)
* [Joint Classification and Trajectory Regression of Online Handwriting Using a Multi-Task Learning Approach](https://openaccess.thecvf.com/content/WACV2022/papers/Ott_Joint_Classification_and_Trajectory_Regression_of_Online_Handwriting_Using_a_WACV_2022_paper.pdf)
* [Semi-Supervised Multi-Task Learning for Semantics and Depth](https://arxiv.org/abs/2110.07197)
## 43.Active Learning(主动学习)
* [Identifying Wrongly Predicted Samples: A Method for Active Learning](https://arxiv.org/abs/2010.06890)
## 42.Landmark Detection(关键点检测)
* [LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity](https://openaccess.thecvf.com/content/WACV2022/papers/Karmali_LEAD_Self-Supervised_Landmark_Estimation_by_Aligning_Distributions_of_Feature_Similarity_WACV_2022_paper.pdf)
* 人体关键点检测
* [Registration of Human Point Set Using Automatic Key Point Detection and Region-Aware Features](https://openaccess.thecvf.com/content/WACV2022/papers/Maharjan_Registration_of_Human_Point_Set_Using_Automatic_Key_Point_Detection_WACV_2022_paper.pdf)
## 41.Action Generation(动作生成)
* [MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion](https://arxiv.org/abs/2110.11460)
:star:[code](https://github.com/skelemoa/mugl):house:[project](https://skeleton.iiit.ac.in/mugl)
* 基于姿势引导的动作合成
* [Pose-Guided Generative Adversarial Net for Novel View Action Synthesis](https://arxiv.org/abs/2110.07993)
:star:[code](https://github.com/xhl-video/PAS-GAN)
## 40.Anomaly Detection(异常检测)
* [CFLOW-AD: Real-Time Unsupervised Anomaly Detection With Localization via Conditional Normalizing Flows](https://openaccess.thecvf.com/content/WACV2022/papers/Gudovskiy_CFLOW-AD_Real-Time_Unsupervised_Anomaly_Detection_With_Localization_via_Conditional_Normalizing_WACV_2022_paper.pdf)
:star:[code](https://github.com/gudovskiy/cflow-ad)
* [A Semi-Supervised Generalized VAE Framework for Abnormality Detection Using One-Class Classification](https://openaccess.thecvf.com/content/WACV2022/papers/Sharma_A_Semi-Supervised_Generalized_VAE_Framework_for_Abnormality_Detection_Using_One-Class_WACV_2022_paper.pdf)
* novelty detection(奇异值检测)
* [OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection](https://arxiv.org/abs/2103.14953)
:star:[code](https://github.com/jewelltaylor/OLED)
## 39.Style Transfer(风格迁移)
* [PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting From Blockwise Training and Skip Connections of High-Frequency Residuals](https://openaccess.thecvf.com/content/WACV2022/papers/Chiu_PhotoWCT2_Compact_Autoencoder_for_Photorealistic_Style_Transfer_Resulting_From_Blockwise_WACV_2022_paper.pdf)
* 3D场景风格化
* [Stylizing 3D Scene via Implicit Representation and HyperNetwork](https://arxiv.org/abs/2105.13016)
:star:[code](https://github.com/ztex08010518/Stylizing-3D-Scene):house:[project](https://ztex08010518.github.io/3dstyletransfer/)
## 38.Sound(音频处理)
* [Beyond Mono to Binaural: Generating Binaural Audio From Mono Audio With Depth and Cross Modal Attention](https://arxiv.org/abs/2111.08046)
:house:[project](https://krantiparida.github.io/projects/bmonobinaural.html)
* 声源定位
* [Unsupervised Sounding Object Localization With Bottom-Up and Top-Down Attention](https://openaccess.thecvf.com/content/WACV2022/papers/Shi_Unsupervised_Sounding_Object_Localization_With_Bottom-Up_and_Top-Down_Attention_WACV_2022_paper.pdf)
:star:[code](https://github.com/VISION-SJTU/USOL)
* [Less Can Be More: Sound Source Localization With a Classification Model](https://openaccess.thecvf.com/content/WACV2022/papers/Senocak_Less_Can_Be_More_Sound_Source_Localization_With_a_Classification_WACV_2022_paper.pdf)
* 声源分离
* [Visually Guided Sound Source Separation and Localization Using Self-Supervised Motion Representations](https://arxiv.org/abs/2104.08506)
:house:[project](https://ly-zhu.github.io/self-supervised-motion-representations)
* [V-SlowFast Network for Efficient Visual Sound Separation](https://openaccess.thecvf.com/content/WACV2022/papers/Zhu_V-SlowFast_Network_for_Efficient_Visual_Sound_Separation_WACV_2022_paper.pdf)
:house:[project](https://ly-zhu.github.io/V-SlowFast)
## 37.Object Tracking(目标跟踪)
* [Intelligent Camera Selection Decisions for Target Tracking in a Camera Network](https://openaccess.thecvf.com/content/WACV2022/papers/Sharma_Intelligent_Camera_Selection_Decisions_for_Target_Tracking_in_a_Camera_WACV_2022_paper.pdf)
* 多目标跟踪
* [Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking](https://openaccess.thecvf.com/content/WACV2022/papers/Zou_Compensation_Tracker_Reprocessing_Lost_Object_for_Multi-Object_Tracking_WACV_2022_paper.pdf)
* 细胞跟踪
* [Consistent Cell Tracking in Multi-Frames With Spatio-Temporal Context by Object-Level Warping Loss](https://openaccess.thecvf.com/content/WACV2022/papers/Hayashida_Consistent_Cell_Tracking_in_Multi-Frames_With_Spatio-Temporal_Context_by_Object-Level_WACV_2022_paper.pdf)
## 36.Soft Biometrics(软生物技术)
* Periocular(眼周) 识别
* [Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition](https://arxiv.org/abs/2111.01325)
## 35.VQA(视觉问答)
* [InfographicVQA](https://arxiv.org/abs/2104.12756)
:star:[code](https://docvqa.org/)
* [Efficient Counterfactual Debiasing for Visual Question Answering](https://openaccess.thecvf.com/content/WACV2022/papers/Kolling_Efficient_Counterfactual_Debiasing_for_Visual_Question_Answering_WACV_2022_paper.pdf)
:star:[code](https://github.com/hengyuan-hu/bottom-up-attention-vqa)
* Audio video scene-aware dialog(视听场景感知对话)
* [QUALIFIER: Question-Guided Self-Attentive Multimodal Fusion Network for Audio Visual Scene-Aware Dialog](https://openaccess.thecvf.com/content/WACV2022/papers/Ye_QUALIFIER_Question-Guided_Self-Attentive_Multimodal_Fusion_Network_for_Audio_Visual_Scene-Aware_WACV_2022_paper.pdf)
## 34.SLAM\Robots
* SLAM
* [HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry](https://arxiv.org/abs/2106.11857)
:star:[code](https://github.com/SpectacularAI/HybVIO)
* [SIGNAV: Semantically-Informed GPS-Denied Navigation and Mapping in Visually-Degraded Environments](https://openaccess.thecvf.com/content/WACV2022/papers/Krasner_SIGNAV_Semantically-Informed_GPS-Denied_Navigation_and_Mapping_in_Visually-Degraded_Environments_WACV_2022_paper.pdf)
* Try-On
* [C-VTON: Context-Driven Image-Based Virtual Try-On Network](https://openaccess.thecvf.com/content/WACV2022/papers/Fele_C-VTON_Context-Driven_Image-Based_Virtual_Try-On_Network_WACV_2022_paper.pdf)
:star:[code](https://github.com/benquick123/C-VTON)
* 3D虚拟试穿
* [Robust 3D Garment Digitization From Monocular 2D Images for 3D Virtual Try-On Systems](https://arxiv.org/abs/2111.15140)
* 时尚属性编辑
* [Tailor Me: An Editing Network for Fashion Attribute Shape Manipulation](https://openaccess.thecvf.com/content/WACV2022/papers/Kwon_Tailor_Me_An_Editing_Network_for_Fashion_Attribute_Shape_Manipulation_WACV_2022_paper.pdf)
* Robots
* 视觉导航
* [Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency](https://arxiv.org/abs/2110.07184)
* [ForeSI: Success-Aware Visual Navigation Agent](https://openaccess.thecvf.com/content/WACV2022/papers/Moghaddam_ForeSI_Success-Aware_Visual_Navigation_Agent_WACV_2022_paper.pdf)
## 33.View Synthesis(视图合成)
* [Revealing Disocclusions in Temporal View Synthesis Through Infilling Vector Prediction](https://arxiv.org/abs/2110.08805)
:star:[code](https://github.com/NagabhushanSN95/IVP):house:[project](https://nagabhushansn95.github.io/publications/2021/ivp.html):tv:[video](https://youtu.be/7IYXKOqP2TA)
* [Fast and Explicit Neural View Synthesis](https://arxiv.org/abs/2107.05775)
* [Novel-View Synthesis of Human Tourist Photos](https://openaccess.thecvf.com/content/WACV2022/papers/Freer_Novel-View_Synthesis_of_Human_Tourist_Photos_WACV_2022_paper.pdf)
## 32.Continual Learning(持续学习)
* [Knowledge Capture and Replay for Continual Learning](https://arxiv.org/abs/2012.06789)
* [Online Continual Learning via Candidates Voting](https://arxiv.org/abs/2110.08855)
## 31.Deepfake Detection(假象检测)
* [BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection](https://arxiv.org/abs/2109.00911)
:star:[code](https://github.com/SamsungSDS-Team9/BiHPF)
## 30.Reinforcement Learning(强化学习)
* [RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation](https://openaccess.thecvf.com/content/WACV2022/papers/Ostonov_RLSS_A_Deep_Reinforcement_Learning_Algorithm_for_Sequential_Scene_Generation_WACV_2022_paper.pdf)
## 29.Image Classification(图像分类)
* [Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals](https://arxiv.org/abs/2009.08270)
* [Class-Balanced Active Learning for Image Classification](https://arxiv.org/abs/2110.04543)
:star:[code](https://github.com/Javadzb/Class-Balanced-AL)
* [Learnable Adaptive Cosine Estimator (LACE) for Image Classification](https://arxiv.org/abs/2110.05324)
:star:[code](https://github.com/GatorSense/LACE)
* [Enhancing Few-Shot Image Classification With Unlabelled Examples](https://arxiv.org/abs/2006.12245)
:star:[code](https://github.com/plai-group/simple-cnaps)
* 零样本分类
* [Trading-Off Information Modalities in Zero-Shot Classification](https://openaccess.thecvf.com/content/WACV2022/papers/Sanchez_Trading-Off_Information_Modalities_in_Zero-Shot_Classification_WACV_2022_paper.pdf)
:star:[code](https://github.com/jadrs/zsl)
* 小样本分类
* [Meta-Learning for Multi-Label Few-Shot Classification](https://arxiv.org/abs/2110.13494)
* 细粒度识别
* [3DRefTransformer: Fine-Grained Object Identification in Real-World Scenes Using Natural Language](https://openaccess.thecvf.com/content/WACV2022/papers/Abdelreheem_3DRefTransformer_Fine-Grained_Object_Identification_in_Real-World_Scenes_Using_Natural_Language_WACV_2022_paper.pdf)
:star:[code](https://github.com/Vision-CAIR/3dreftransformer):house:[project](https://vision-cair.github.io/3dreftransformer/)
## 28.Pose Estimation(姿态估计)
* 物品姿势估计
* [Occlusion-Robust Object Pose Estimation with Holistic Representation](https://arxiv.org/abs/2110.11636)
:star:[code](https://github.com/BoChenYS/ROPE)
* Object Pose Refinement
* [SporeAgent: Reinforced Scene-Level Plausibility for Object Pose Refinement](https://openaccess.thecvf.com/content/WACV2022/papers/Bauer_SporeAgent_Reinforced_Scene-Level_Plausibility_for_Object_Pose_Refinement_WACV_2022_paper.pdf)
:star:[code](https://github.com/dornik/sporeagent)
* 动物姿势
* [Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation](https://openaccess.thecvf.com/content/WACV2022/papers/Rashid_Equine_Pain_Behavior_Classification_via_Self-Supervised_Disentangled_Pose_Representation_WACV_2022_paper.pdf)
## 27.Defect Detection(缺陷检测)
* [Fully Convolutional Cross-Scale-Flows for Image-Based Defect Detection](https://arxiv.org/abs/2110.02855)
:star:[code](https://github.com/marco-rudolph/cs-flow)
* [Automated Defect Inspection in Reverse Engineering of Integrated Circuits](https://openaccess.thecvf.com/content/WACV2022/papers/Bette_Automated_Defect_Inspection_in_Reverse_Engineering_of_Integrated_Circuits_WACV_2022_paper.pdf)
* 下水道缺陷分类
* [Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder](https://arxiv.org/abs/2111.07846)
:star:[code](https://bitbucket.org/aauvap/ctgnn/src/master/):house:[project](https://vap.aau.dk/ctgnn/)
## 26.Dataset\Benchmark(数据集\基准)
* [MovingFashion: A Benchmark for the Video-To-Shop Challenge](https://arxiv.org/abs/2110.02627)
:sunflower:[dataset](https://github.com/HumaticsLAB/SEAM-Match-RCNN)
* [Challenges in Procedural Multimodal Machine Comprehension: A Novel Way To Benchmark](https://arxiv.org/abs/2110.11899)
* 用于检测跟踪海域人类
* [SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water](https://arxiv.org/abs/2105.01922)
:sunflower:[dataset](https://seadronessee.cs.uni-tuebingen.de/)
* 图像识别
* [Danish Fungi 2020 - Not Just Another Image Recognition Dataset](https://openaccess.thecvf.com/content/WACV2022/papers/Picek_Danish_Fungi_2020_-_Not_Just_Another_Image_Recognition_Dataset_WACV_2022_paper.pdf)
:sunflower:[dataset](https://sites.google.com/view/danish-fungi-dataset)
* 自动驾驶
* [DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception](https://arxiv.org/abs/2110.07790)
:house:[project](https://goodproj13.github.io/DGL-MOTS/):tv:[video](https://youtu.be/5WVB8_TqMKQ)
* 用于从高空鱼眼相机中检测和跟踪行人
* [WEPDTOF: A Dataset and Benchmark Algorithms for In-the-Wild People Detection and Tracking From Overhead Fisheye Cameras](https://openaccess.thecvf.com/content/WACV2022/papers/Tezcan_WEPDTOF_A_Dataset_and_Benchmark_Algorithms_for_In-the-Wild_People_Detection_WACV_2022_paper.pdf)
## 25.Image Captioning(图像字幕)
* [Is an Image Worth Five Sentences? A New Look Into Semantics for Image-Text Matching](https://openaccess.thecvf.com/content/WACV2022/papers/Biten_Is_an_Image_Worth_Five_Sentences_A_New_Look_Into_WACV_2022_paper.pdf)
:star:[code](https://github.com/furkanbiten/ncs_metric):star:[code](https://github.com/andrespmd/semantic_adaptive_margin)
* [Let There Be a Clock on the Beach: Reducing Object Hallucination in Image Captioning](https://openaccess.thecvf.com/content/WACV2022/papers/Biten_Let_There_Be_a_Clock_on_the_Beach_Reducing_Object_WACV_2022_paper.pdf)
:star:[code](https://github.com/furkanbiten/object-bias)
* [Improve Image Captioning by Estimating the Gazing Patterns From the Caption](https://openaccess.thecvf.com/content/WACV2022/papers/Alahmadi_Improve_Image_Captioning_by_Estimating_the_Gazing_Patterns_From_the_WACV_2022_paper.pdf)
## 24.Image Retrieval(图像检索)
* [All the Attention You Need: Global-Local, Spatial-Channel Attention for Image Retrieval](https://arxiv.org/abs/2107.08000)
* [Learning With Label Noise for Image Retrieval by Selecting Interactions](https://arxiv.org/abs/2112.10453)
* [SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval](https://arxiv.org/abs/2009.01485)
* Image-Text retrieval
* [GraDual: Graph-Based Dual-Modal Representation for Image-Text Matching](https://openaccess.thecvf.com/content/WACV2022/papers/Long_GraDual_Graph-Based_Dual-Modal_Representation_for_Image-Text_Matching_WACV_2022_paper.pdf)
* 图像搜索
* [Generating and Controlling Diversity in Image Search](https://openaccess.thecvf.com/content/WACV2022/papers/Tanjim_Generating_and_Controlling_Diversity_in_Image_Search_WACV_2022_paper.pdf)
* 视频文本匹配
* [Video and Text Matching With Conditioned Embeddings](https://arxiv.org/abs/2110.11298)
:star:[code](https://github.com/AmeenAli/VideoMatch)
* 绘图检索
* [DeepPatent: Large scale patent drawing recognition and retrieval](https://openaccess.thecvf.com/content/WACV2022/papers/Kucer_DeepPatent_Large_Scale_Patent_Drawing_Recognition_and_Retrieval_WACV_2022_paper.pdf)
* 视频检索
* [Masking Modalities for Cross-Modal Video Retrieval](https://arxiv.org/abs/2111.01300)
## 23.Autonomous Driving(智能驾驶)
* 自动驾驶
* [Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving](https://arxiv.org/abs/2008.11901)
* 目标检测
* [Adversarial Robustness of Deep Sensor Fusion Models](https://arxiv.org/abs/2006.13192)
* 车辆定位
* [CoordiNet: Uncertainty-Aware Pose Regressor for Reliable Vehicle Localization](https://arxiv.org/abs/2103.10796)
* Vehicle Detection(交通检测)
* 基于航空图像的交通监控
* [AirCamRTM: Enhancing Vehicle Detection for Efficient Aerial Camera-Based Road Traffic Monitoring](https://openaccess.thecvf.com/content/WACV2022/papers/Makrigiorgis_AirCamRTM_Enhancing_Vehicle_Detection_for_Efficient_Aerial_Camera-Based_Road_Traffic_WACV_2022_paper.pdf)
* Lane Detection(车道线检测)
* [Robust Lane Detection via Expanded Self Attention](https://arxiv.org/abs/2102.07037)
## 22.Human Action Recognition(人体动作识别与检测)
* [NUTA: Non-Uniform Temporal Aggregation for Action Recognition](https://arxiv.org/abs/2012.08041)
* [MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition](https://openaccess.thecvf.com/content/WACV2022/papers/Chen_MM-ViT_Multi-Modal_Video_Transformer_for_Compressed_Video_Action_Recognition_WACV_2022_paper.pdf)
* [Dual-Head Contrastive Domain Adaptation for Video Action Recognition](https://openaccess.thecvf.com/content/WACV2022/papers/da_Costa_Dual-Head_Contrastive_Domain_Adaptation_for_Video_Action_Recognition_WACV_2022_paper.pdf)
:star:[code](https://github.com/vturrisi/CO2A)
* [Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition](https://openaccess.thecvf.com/content/WACV2022/papers/Memmesheimer_Skeleton-DML_Deep_Metric_Learning_for_Skeleton-Based_One-Shot_Action_Recognition_WACV_2022_paper.pdf)
:star:[code](https://github.com/raphaelmemmesheimer/skeleton-dml)
* [SWAG-V: Explanations for Video Using Superpixels Weighted by Average Gradients](https://openaccess.thecvf.com/content/WACV2022/papers/Hartley_SWAG-V_Explanations_for_Video_Using_Superpixels_Weighted_by_Average_Gradients_WACV_2022_paper.pdf)
* [Pose and Joint-Aware Action Recognition](https://arxiv.org/abs/2010.08164)
:star:[code](https://github.com/anshulbshah/PoseAction)
* [Domain Generalization Through Audio-Visual Relative Norm Alignment in First Person Action Recognition](https://arxiv.org/abs/2110.10101)
* 3D动作识别
* [How and What To Learn: Taxonomizing Self-Supervised Learning for 3D Action Recognition](https://openaccess.thecvf.com/content/WACV2022/papers/Tanfous_How_and_What_To_Learn_Taxonomizing_Self-Supervised_Learning_for_3D_WACV_2022_paper.pdf)(https://github.com/serre-lab/ssl_actionrec)
* 动作定位
* [Towards Active Vision for Action Localization With Reactive Control and Predictive Learning](https://arxiv.org/abs/2111.05448)
* [Contextual Proposal Network for Action Localization](https://openaccess.thecvf.com/content/WACV2022/papers/Hsieh_Contextual_Proposal_Network_for_Action_Localization_WACV_2022_paper.pdf)
* 时序动作分割
* [SSCAP: Self-Supervised Co-Occurrence Action Parsing for Unsupervised Temporal Action Segmentation](https://arxiv.org/abs/2105.14158)
* [Leaky Gated Cross-Attention for Weakly Supervised Multi-Modal Temporal Action Localization](https://openaccess.thecvf.com/content/WACV2022/papers/Lee_Leaky_Gated_Cross-Attention_for_Weakly_Supervised_Multi-Modal_Temporal_Action_Localization_WACV_2022_paper.pdf)
## 21.Point Cloud(点云)
* [Surrogate Model-Based Explainability Methods for Point Cloud NNs](https://arxiv.org/abs/2107.13459)
:star:[code](https://github.com/Explain3D/LIME-3D)
* [StickyLocalization: Robust End-to-End Relocalization on Point Clouds Using Graph Neural Networks](https://openaccess.thecvf.com/content/WACV2022/papers/Fischer_StickyLocalization_Robust_End-to-End_Relocalization_on_Point_Clouds_Using_Graph_Neural_WACV_2022_paper.pdf)
* 3D 点云
* [Spatial-Temporal Transformer for 3D Point Cloud Sequences](https://arxiv.org/abs/2110.09783)
* [Biomass Prediction With 3D Point Clouds From LiDAR](https://openaccess.thecvf.com/content/WACV2022/papers/Pan_Biomass_Prediction_With_3D_Point_Clouds_From_LiDAR_WACV_2022_paper.pdf)
* 3D点云目标分类
* [What Makes for Effective Few-Shot Point Cloud Classification?](https://openaccess.thecvf.com/content/WACV2022/papers/Ye_What_Makes_for_Effective_Few-Shot_Point_Cloud_Classification_WACV_2022_paper.pdf)
* 分类与分割
* [EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation](https://arxiv.org/abs/2103.02517)
## 20.Transformer
* [Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations](https://arxiv.org/abs/2108.05887)
* [Visualizing Paired Image Similarity in Transformer Networks](https://openaccess.thecvf.com/content/WACV2022/papers/Black_Visualizing_Paired_Image_Similarity_in_Transformer_Networks_WACV_2022_paper.pdf)
:star:[code](https://github.com/vidarlab/xformer-paired-viz)
* [S2-MLP: Spatial-Shift MLP Architecture for Vision](https://openaccess.thecvf.com/content/WACV2022/papers/Yu_S2-MLP_Spatial-Shift_MLP_Architecture_for_Vision_WACV_2022_paper.pdf)
* 图像分类
* [Resource-Efficient Hybrid X-Formers for Vision](https://openaccess.thecvf.com/content/WACV2022/papers/Jeevan_Resource-Efficient_Hybrid_X-Formers_for_Vision_WACV_2022_paper.pdf)
:star:[code](https://github.com/pranavphoenix/VisionXformer)
* 图像超级补全
* [Image-Adaptive Hint Generation via Vision Transformer for Outpainting](https://openaccess.thecvf.com/content/WACV2022/papers/Kong_Image-Adaptive_Hint_Generation_via_Vision_Transformer_for_Outpainting_WACV_2022_paper.pdf)
## 19.Model Compression\Knowledge Distillation\Pruning(模型压缩\知识蒸馏\剪枝)
* 模型压缩
* [Model Compression Using Optimal Transport](https://arxiv.org/abs/2012.03907)
* 知识蒸馏
* [Extractive Knowledge Distillation](https://openaccess.thecvf.com/content/WACV2022/papers/Kobayashi_Extractive_Knowledge_Distillation_WACV_2022_paper.pdf)
* [Self-Guidance: Improve Deep Neural Network Generalization via Knowledge Distillation](https://openaccess.thecvf.com/content/WACV2022/papers/Zheng_Self-Guidance_Improve_Deep_Neural_Network_Generalization_via_Knowledge_Distillation_WACV_2022_paper.pdf)
* [Online Knowledge Distillation by Temporal-Spatial Boosting](https://openaccess.thecvf.com/content/WACV2022/papers/Li_Online_Knowledge_Distillation_by_Temporal-Spatial_Boosting_WACV_2022_paper.pdf)
* [Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data](https://arxiv.org/abs/2108.05698)
* 剪枝
* [Hessian-Aware Pruning and Optimal Neural Implant](http://arxiv.org/abs/2101.08940)
:star:[code](https://github.com/yaozhewei/HAP)
* [Channel Pruning via Lookahead Search Guided Reinforcement Learning](https://openaccess.thecvf.com/content/WACV2022/papers/Wang_Channel_Pruning_via_Lookahead_Search_Guided_Reinforcement_Learning_WACV_2022_paper.pdf)
* [EZCrop: Energy-Zoned Channels for Robust Output Pruning](https://arxiv.org/abs/2105.03679)
:star:[code](https://github.com/ruilin0212/EZCrop)
## 18.NAS(神经架构搜索)
* [Approximate Neural Architecture Search via Operation Distribution Learning](https://openaccess.thecvf.com/content/WACV2022/papers/Wan_Approximate_Neural_Architecture_Search_via_Operation_Distribution_Learning_WACV_2022_paper.pdf)
* [Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo](https://arxiv.org/abs/2110.05621)
* [Towards a Robust Differentiable Architecture Search Under Label Noise](https://arxiv.org/abs/2110.12197)
* [Lightweight Monocular Depth With a Novel Neural Architecture Search Method](https://arxiv.org/abs/2108.11105)
* [Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search](https://arxiv.org/abs/2005.07564)
## 17.OCR(文本检测)
* [Post-OCR Paragraph Recognition by Graph Convolutional Networks](https://arxiv.org/abs/2101.12741)
* 不规则场景文本识别
* [Robustly Recognizing Irregular Scene Text by Rectifying Principle Irregularities](https://openaccess.thecvf.com/content/WACV2022/papers/Xu_Robustly_Recognizing_Irregular_Scene_Text_by_Rectifying_Principle_Irregularities_WACV_2022_paper.pdf)
* LOGO识别
* [SeeTek: Very Large-Scale Open-Set Logo Recognition With Text-Aware Metric Learning](https://openaccess.thecvf.com/content/WACV2022/papers/Li_SeeTek_Very_Large-Scale_Open-Set_Logo_Recognition_With_Text-Aware_Metric_Learning_WACV_2022_paper.pdf)
* 手写文本识别
* [One-Shot Compositional Data Generation for Low Resource Handwritten Text Recognition](https://arxiv.org/abs/2105.05300)
* 表格结构识别
* [Visual Understanding of Complex Table Structures from Document Images](https://arxiv.org/abs/2111.07129)
## 16.Super-Resolution(超分辨率)
* [Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-Resolution](https://arxiv.org/abs/2111.03649)
:star:[code](https://github.com/andreas128/AdFlow?1)
* [Multi-Dimensional Dynamic Model Compression for Efficient Image Super-Resolution](https://openaccess.thecvf.com/content/WACV2022/papers/Hou_Multi-Dimensional_Dynamic_Model_Compression_for_Efficient_Image_Super-Resolution_WACV_2022_paper.pdf)
* [edge-SR: Super-Resolution for the Masses](https://openaccess.thecvf.com/content/WACV2022/papers/Michelini_edge-SR_Super-Resolution_for_the_Masses_WACV_2022_paper.pdf)
* [DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks](https://arxiv.org/abs/2012.11230)
* [Hyperspectral Image Super-Resolution With RGB Image Super-Resolution as an Auxiliary Task](https://openaccess.thecvf.com/content/WACV2022/papers/Li_Hyperspectral_Image_Super-Resolution_With_RGB_Image_Super-Resolution_as_an_Auxiliary_WACV_2022_paper.pdf)
:star:[code](https://github.com/kli8996/HSISR)
* VSR
* [MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution](https://arxiv.org/abs/2110.15327)
* BSR
* [MoESR: Blind Super-Resolution Using Kernel-Aware Mixture of Experts](https://openaccess.thecvf.com/content/WACV2022/papers/Emad_MoESR_Blind_Super-Resolution_Using_Kernel-Aware_Mixture_of_Experts_WACV_2022_paper.pdf)
:star:[code](https://github.com/memad73/MoESR)
## 15.Image Synthesis(图像合成)
* 图像生成
* [StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation](https://arxiv.org/abs/2112.08493)
:star:[code](https://github.com/catlab-team/stylemc):house:[project](https://catlab-team.github.io/stylemc/):tv:[video](https://youtu.be/ILm_5tvtzPI)
* sketch-to-photo
* [Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis](https://arxiv.org/abs/2104.05703)
:star:[code](https://github.com/Mukosame/AODA)
* Image-to-Image Translation
* [Evaluation of Correctness in Unsupervised Many-to-Many Image Translation](https://arxiv.org/abs/2103.15727)
:star:[code](https://github.com/dbash/umi2i_correctness)
## 14.Un\Self\Semi-Supervised Learning(无\自\半监督学习)
* 半监督
* [HierMatch: Leveraging Label Hierarchies for Improving Semi-Supervised Learning](https://arxiv.org/abs/2111.00164)
:star:[code](https://github.com/07Agarg/HIERMATCH)
* 自监督
* [Boosting Contrastive Self-Supervised Learning with False Negative Cancellation](https://arxiv.org/abs/2011.11765)
:star:[code](https://github.com/google-research/fnc)
* [Self-Supervised Shape Alignment for Sports Field Registration](https://openaccess.thecvf.com/content/WACV2022/papers/Shi_Self-Supervised_Shape_Alignment_for_Sports_Field_Registration_WACV_2022_paper.pdf)
* 无监督
* [Unsupervised Learning for Human Sensing Using Radio Signals](https://openaccess.thecvf.com/content/WACV2022/papers/Li_Unsupervised_Learning_for_Human_Sensing_Using_Radio_Signals_WACV_2022_paper.pdf)
## 13.Image Segmentation(图像分割)
* [Semantically Stealthy Adversarial Attacks Against Segmentation Models](https://arxiv.org/abs/2104.01732)
* 视频分割
* [D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos](https://openaccess.thecvf.com/content/WACV2022/papers/Schmidt_D2Conv3D_Dynamic_Dilated_Convolutions_for_Object_Segmentation_in_Videos_WACV_2022_paper.pdf)
:star:[code](https://github.com/Schmiddo/d2conv3d)
* [Perceptual Consistency in Video Segmentation](https://arxiv.org/abs/2110.12385)
* [Temporally Stable Video Segmentation Without Video Annotations](https://arxiv.org/abs/2110.08893)
* VOS(视频目标分割)
* [Pixel-Level Bijective Matching for Video Object Segmentation](https://arxiv.org/abs/2110.01644)
:star:[code](https://github.com/suhwan-cho/BMVOS)
* 动作分割
* [Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional Videos](https://arxiv.org/abs/2110.05697)
:star:[code](https://github.com/rezaghoddoosian/Hierarchical-Task-Modeling)
* 语义分割
* [Plugging Self-Supervised Monocular Depth Into Unsupervised Domain Adaptation for Semantic Segmentation](https://arxiv.org/abs/2110.06685)
:star:[code](https://github.com/CVLAB-Unibo/d4-dbst)
* [Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation](https://arxiv.org/abs/2106.04144)
:star:[code](https://github.com/gabriel-tjio/ASH)
* [Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries](https://arxiv.org/abs/2110.02833)
:star:[code](https://github.com/CVLAB-Unibo/Shallow_DA)
* [Evaluating the Robustness of Semantic Segmentation for Autonomous Driving Against Real-World Adversarial Patch Attacks](https://arxiv.org/abs/2108.06179)
* [Multi-Domain Incremental Learning for Semantic Segmentation](https://arxiv.org/abs/2110.12205)
:star:[code](https://github.com/prachigarg23/MDIL-SS)
* [Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images](https://arxiv.org/abs/2110.07782)
:star:[code](https://github.com/immuno121/ALS4GAN)
* [Multi-Domain Semantic Segmentation With Overlapping Labels](https://openaccess.thecvf.com/content/WACV2022/papers/Bevandic_Multi-Domain_Semantic_Segmentation_With_Overlapping_Labels_WACV_2022_paper.pdf)
* [Mixed-Dual-Head Meets Box Priors: A Robust Framework for Semi-Supervised Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Chen_Mixed-Dual-Head_Meets_Box_Priors_A_Robust_Framework_for_Semi-Supervised_Segmentation_WACV_2022_paper.pdf)
* 视频语义分割
* [AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation](https://arxiv.org/abs/2110.12369)
* 弱监督语义分割
* [Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation](https://arxiv.org/abs/2110.14309)
:star:[code](https://github.com/weixuansun/InferCam)
* 无监督语义分割
* [Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation](https://arxiv.org/abs/2102.13002)
* 半监督语义分割
* [Semi-Supervised Semantic Segmentation of Vessel Images Using Leaking Perturbations](https://arxiv.org/abs/2110.11998)
* 小样本语义分割
* [Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation](https://arxiv.org/abs/2110.11650)
:star:[code](https://github.com/taveraantonio/PixDA)
* [A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation](https://arxiv.org/abs/2111.01418)
* [MaskSplit: Self-Supervised Meta-Learning for Few-Shot Semantic Segmentation](https://arxiv.org/abs/2110.12207)
* 实例分割
* [In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Weyler_In-Field_Phenotyping_Based_on_Crop_Leaf_and_Plant_Instance_Segmentation_WACV_2022_paper.pdf)
:star:[code](https://github.com/PRBonn/leaf-plant-instance-segmentation)
* [FASSST: Fast Attention Based Single-Stage Segmentation Net for Real-Time Instance Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Cheng_FASSST_Fast_Attention_Based_Single-Stage_Segmentation_Net_for_Real-Time_Instance_WACV_2022_paper.pdf)
* 全景分割
* [Single-Shot Path Integrated Panoptic Segmentation](https://arxiv.org/abs/2012.01632)
* 视频全景分割
* [Time-Space Transformers for Video Panoptic Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Petrovai_Time-Space_Transformers_for_Video_Panoptic_Segmentation_WACV_2022_paper.pdf)
* Foreground-Background 分割
* [Learning Foreground-Background Segmentation from Improved Layered GANs](https://arxiv.org/abs/2104.00483)
* 超像素分割
* [HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Peng_HERS_Superpixels_Deep_Affinity_Learning_for_Hierarchical_Entropy_Rate_Segmentation_WACV_2022_paper.pdf)
:star:[code](https://github.com/hankuipeng/DAL-HERS)
* 道路分割
* [VCSeg: Virtual Camera Adaptation for Road Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Cheng_VCSeg_Virtual_Camera_Adaptation_for_Road_Segmentation_WACV_2022_paper.pdf)
* 抠图
* 视频抠图
* [Robust High-Resolution Video Matting with Temporal Guidance](https://arxiv.org/abs/2108.11515)
:star:[code](https://github.com/PeterL1n/RobustVideoMatting):house:[project](https://peterl1n.github.io/RobustVideoMatting/#/):tv:[video](https://youtu.be/Ay-mGCEYEzM)
## 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
* [Unsupervised Robust Domain Adaptation Without Source Data](https://arxiv.org/abs/2103.14577)
* 半监督域适应
* [Semi-Supervised Domain Adaptation via Sample-to-Sample Self-Distillation](https://arxiv.org/abs/2111.14353)
* 无监督域适应
* [Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation](https://openaccess.thecvf.com/content/WACV2022/papers/Ahmed_Cleaning_Noisy_Labels_by_Negative_Ensemble_Learning_for_Source-Free_Unsupervised_WACV_2022_paper.pdf)
* [Adversarial Branch Architecture Search for Unsupervised Domain Adaptation](https://arxiv.org/abs/2102.06679)
:star:[code](https://github.com/lr94/abas)
* 开集域适应
* [Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation](https://arxiv.org/abs/2107.02067)
:star:[code](https://github.com/silvia1993/HyMOS)
* 多源域适应
* [Mutual Learning of Joint and Separate Domain Alignments for Multi-Source Domain Adaptation](https://openaccess.thecvf.com/content/WACV2022/papers/Xu_Mutual_Learning_of_Joint_and_Separate_Domain_Alignments_for_Multi-Source_WACV_2022_paper.pdf)
* [Coupled Training for Multi-Source Domain Adaptation](https://openaccess.thecvf.com/content/WACV2022/papers/Amosy_Coupled_Training_for_Multi-Source_Domain_Adaptation_WACV_2022_paper.pdf)
:star:[code](https://github.com/amosy3/MUST)
* 多目标域适应
* [Federated Multi-Target Domain Adaptation](https://arxiv.org/abs/2108.07792)
* 域泛化
* [Learning to Weight Filter Groups for Robust Classification](https://openaccess.thecvf.com/content/WACV2022/papers/Yuan_Learning_to_Weight_Filter_Groups_for_Robust_Classification_WACV_2022_paper.pdf)
* 零样本域泛化
* [COCOA: Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains](https://openaccess.thecvf.com/content/WACV2022/papers/Mangla_COCOA_Context-Conditional_Adaptation_for_Recognizing_Unseen_Classes_in_Unseen_Domains_WACV_2022_paper.pdf)
* 小样本学习
* [Contextual Gradient Scaling for Few-Shot Learning](https://arxiv.org/abs/2110.10353)
:star:[code](https://github.com/shlee625/CxGrad)
* [Calibrating CNNs for Few-Shot Meta Learning](https://openaccess.thecvf.com/content/WACV2022/papers/Yang_Calibrating_CNNs_for_Few-Shot_Meta_Learning_WACV_2022_paper.pdf)
* [SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning](https://arxiv.org/abs/2111.04316)
:star:[code](https://github.com/MartaYang/SEGA)
* [Tensor Feature Hallucination for Few-Shot Learning](https://arxiv.org/abs/2106.05321)
:star:[code](https://github.com/MichalisLazarou/TFH_fewshot)
* [Ortho-Shot: Low Displacement Rank Regularization With Data Augmentation for Few-Shot Learning](https://openaccess.thecvf.com/content/WACV2022/papers/Osahor_Ortho-Shot_Low_Displacement_Rank_Regularization_With_Data_Augmentation_for_Few-Shot_WACV_2022_paper.pdf)
* Domain Shift
* [Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift](https://openaccess.thecvf.com/content/WACV2022/papers/Baktashmotlagh_Learning_To_Generate_the_Unknowns_as_a_Remedy_to_the_WACV_2022_paper.pdf)
* 单样本学习
* [Meta-Meta Classification for One-Shot Learning](https://arxiv.org/abs/2004.08083)
## 11.Face(人脸)
* 3D Facial
* 人脸表情
* [Information Bottlenecked Variational Autoencoder for Disentangled 3D Facial Expression Modelling](https://openaccess.thecvf.com/content/WACV2022/papers/Sun_Information_Bottlenecked_Variational_Autoencoder_for_Disentangled_3D_Facial_Expression_Modelling_WACV_2022_paper.pdf)
* 3D人脸重建
* [Occlusion Resistant Network for 3D Face Reconstruction](https://openaccess.thecvf.com/content/WACV2022/papers/Tiwari_Occlusion_Resistant_Network_for_3D_Face_Reconstruction_WACV_2022_paper.pdf)
* 基于皱纹的人体识别
* [Mobile Based Human Identification Using Forehead Creases: Application and Assessment Under COVID-19 Masked Face Scenarios](https://openaccess.thecvf.com/content/WACV2022/papers/Bharadwaj_Mobile_Based_Human_Identification_Using_Forehead_Creases_Application_and_Assessment_WACV_2022_paper.pdf)
* 人脸活体检测
* [Disentangled Representation With Dual-Stage Feature Learning for Face Anti-Spoofing](https://arxiv.org/abs/2110.09157)
* 人脸表情
* [Quantified Facial Expressiveness for Affective Behavior Analytics](https://arxiv.org/abs/2110.01758)
* [Detection and Localization of Facial Expression Manipulations](https://openaccess.thecvf.com/content/WACV2022/papers/Mazaheri_Detection_and_Localization_of_Facial_Expression_Manipulations_WACV_2022_paper.pdf)
* 人脸检测
* [Measuring Representation of Race, Gender, and Age in Children's Books: Face Detection and Feature Classification in Illustrated Images](https://openaccess.thecvf.com/content/WACV2022/papers/Szasz_Measuring_Representation_of_Race_Gender_and_Age_in_Childrens_Books_WACV_2022_paper.pdf)
* PAD人脸呈现攻击检测
* [Learnable Multi-Level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection](https://arxiv.org/abs/2109.07950)
:star:[code](https://github.com/meilfang/LMFD-PAD)
* [Digital and Physical-World Attacks on Remote Pulse Detection](https://arxiv.org/abs/2110.11525)
* 年龄预测
* [Fair and Accurate Age Prediction Using Distribution Aware Data Curation and Augmentation](https://arxiv.org/abs/2009.05283)
:star:[code](https://github.com/ForBlindRev/AIBias)
* Face verification(人脸验证)
* [Face Verification With Challenging Imposters and Diversified Demographics](https://openaccess.thecvf.com/content/WACV2022/papers/Popescu_Face_Verification_With_Challenging_Imposters_and_Diversified_Demographics_WACV_2022_paper.pdf)
:star:[code](https://github.com/AIMultimediaLab/FaVCI2D-Face-Verification-with-Challenging-Imposters-and-Diversified-Demographics)
* [On Black-Box Explanation for Face Verification](https://openaccess.thecvf.com/content/WACV2022/papers/Mery_On_Black-Box_Explanation_for_Face_Verification_WACV_2022_paper.pdf)
* 人脸去模糊
* [Deep Feature Prior Guided Face Deblurring](https://openaccess.thecvf.com/content/WACV2022/papers/Jung_Deep_Feature_Prior_Guided_Face_Deblurring_WACV_2022_paper.pdf)
* facial forgery detection
* [Generalized Facial Manipulation Detection With Edge Region Feature Extraction](https://arxiv.org/abs/2102.01381)
* 人脸图像质量苹果
* [A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics](https://arxiv.org/abs/2110.11111)
* 人脸补全
* [3DFaceFill: An Analysis-By-Synthesis Approach to Face Completion](https://arxiv.org/abs/2110.10395)
* 妆容迁移
* [Facial Attribute Transformers for Precise and Robust Makeup Transfer](https://openaccess.thecvf.com/content/WACV2022/papers/Wan_Facial_Attribute_Transformers_for_Precise_and_Robust_Makeup_Transfer_WACV_2022_paper.pdf)
* 人脸恢复
* [Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View Image](https://openaccess.thecvf.com/content/WACV2022/papers/Ju_Complete_Face_Recovery_GAN_Unsupervised_Joint_Face_Rotation_and_De-Occlusion_WACV_2022_paper.pdf)
:star:[code](https://github.com/yeongjoonJu/CFR-GAN)
* 人脸识别
* [Measuring Hidden Bias Within Face Recognition via Racial Phenotypes](https://arxiv.org/abs/2110.09839)
* [Geometrically Adaptive Dictionary Attack on Face Recognition](https://arxiv.org/abs/2111.04371)
## 10.Adversarial Learning(对抗学习)
* 黑盒攻击
* [On the Effectiveness of Small Input Noise for Defending Against Query-Based Black-Box Attacks](https://arxiv.org/abs/2101.04829)
* 对抗样本
* [Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis](https://arxiv.org/abs/2012.06405)
* 对抗攻击
* [Generative Adversarial Attack on Ensemble Clustering](https://openaccess.thecvf.com/content/WACV2022/papers/Kumar_Generative_Adversarial_Attack_on_Ensemble_Clustering_WACV_2022_paper.pdf)
## 9.Remote Sensing\Satellite Image(遥感\卫星图像)
* [Lane-Level Street Map Extraction From Aerial Imagery](https://openaccess.thecvf.com/content/WACV2022/papers/He_Lane-Level_Street_Map_Extraction_From_Aerial_Imagery_WACV_2022_paper.pdf)
* [An Experimental Comparison of Multi-View Stereo Approaches on Satellite Images](https://openaccess.thecvf.com/content/WACV2022/papers/Gomez_An_Experimental_Comparison_of_Multi-View_Stereo_Approaches_on_Satellite_Images_WACV_2022_paper.pdf)
* 小样本开放集识别
* [Few-Shot Open-Set Recognition of Hyperspectral Images With Outlier Calibration Network](https://openaccess.thecvf.com/content/WACV2022/papers/Pal_Few-Shot_Open-Set_Recognition_of_Hyperspectral_Images_With_Outlier_Calibration_Network_WACV_2022_paper.pdf)
:star:[code](https://github.com/DebabrataPal7/OCN)
* 检测
* [Physical Adversarial Attacks on an Aerial Imagery Object Detector](https://arxiv.org/abs/2108.11765)
:tv:[video](https://www.youtube.com/watch?v=5N6JDZf3pLQ)
* 停车场检测
* [A Context-Enriched Satellite Imagery Dataset and an Approach for Parking Lot Detection](https://openaccess.thecvf.com/content/WACV2022/papers/Yin_A_Context-Enriched_Satellite_Imagery_Dataset_and_an_Approach_for_Parking_WACV_2022_paper.pdf)
* 跟踪
* [Siamese Transformer Pyramid Networks for Real-Time UAV Tracking](https://arxiv.org/abs/2110.08822)
:star:[code](https://github.com/RISCNYUAD/SiamTPNTracker)
## 8.Image Processing(图像处理)
* [Extracting Vignetting and Grain Filter Effects From Photos](https://openaccess.thecvf.com/content/WACV2022/papers/Abdelhamed_Extracting_Vignetting_and_Grain_Filter_Effects_From_Photos_WACV_2022_paper.pdf)
* 去噪
* [Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-Electron Microscopy](https://openaccess.thecvf.com/content/WACV2022/papers/Huang_Weakly_Supervised_Learning_for_Joint_Image_Denoising_and_Protein_Localization_WACV_2022_paper.pdf)
* 去雨
* [FLUID: Few-Shot Self-Supervised Image Deraining](https://openaccess.thecvf.com/content/WACV2022/papers/Nandan_FLUID_Few-Shot_Self-Supervised_Image_Deraining_WACV_2022_paper.pdf)
* [Single Image Deraining Network With Rain Embedding Consistency and Layered LSTM](https://arxiv.org/abs/2111.03615)
:star:[code](https://github.com/Yizhou-Li-CV/ECNet)
* 去模糊
* [Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach With Kernel Parameterization](https://openaccess.thecvf.com/content/WACV2022/papers/Guan_Non-Blind_Deblurring_for_Fluorescence_A_Deformable_Latent_Space_Approach_With_WACV_2022_paper.pdf)
* [Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning](https://arxiv.org/abs/2108.05251)
:star:[code](https://github.com/Abdullah-Abuolaim/multi-task-defocus-deblurring-dual-pixel-nimat)
* 去马赛克
* [Forgery Detection by Internal Positional Learning of Demosaicing Traces](https://openaccess.thecvf.com/content/WACV2022/papers/Bammey_Forgery_Detection_by_Internal_Positional_Learning_of_Demosaicing_Traces_WACV_2022_paper.pdf)
* 图像着色
* [Late-Resizing: A Simple but Effective Sketch Extraction Strategy for Improving Generalization of Line-Art Colorization](https://openaccess.thecvf.com/content/WACV2022/papers/Kim_Late-Resizing_A_Simple_but_Effective_Sketch_Extraction_Strategy_for_Improving_WACV_2022_paper.pdf)
* [Pro-CCaps: Progressively Teaching Colourisation to Capsules](https://openaccess.thecvf.com/content/WACV2022/papers/Pucci_Pro-CCaps_Progressively_Teaching_Colourisation_to_Capsules_WACV_2022_paper.pdf)
:star:[code](https://github.com/Riretta/Pro_CCaps-Progressive-learning-with-capsules)
* 图像裁剪
* [Auditing Saliency Cropping Algorithms](https://openaccess.thecvf.com/content/WACV2022/papers/Birhane_Auditing_Saliency_Cropping_Algorithms_WACV_2022_paper.pdf)
* [Re-Compose the Image by Evaluating the Crop on More Than Just a Score](https://openaccess.thecvf.com/content/WACV2022/papers/Cheng_Re-Compose_the_Image_by_Evaluating_the_Crop_on_More_Than_WACV_2022_paper.pdf)
* 图像恢复
* [Training a Task-Specific Image Reconstruction Loss](https://arxiv.org/abs/2103.14616)
* [Image Restoration by Deep Projected GSURE](https://openaccess.thecvf.com/content/WACV2022/papers/Abu-Hussein_Image_Restoration_by_Deep_Projected_GSURE_WACV_2022_paper.pdf)
* 图像修复
* [Resolution-Robust Large Mask Inpainting With Fourier Convolutions](https://arxiv.org/abs/2109.07161)
:star:[code](https://github.com/saic-mdal/lama)
* 图像降质
* [Deep Photo Scan: Semi-Supervised Learning for dealing with the real-world degradation in Smartphone Photo Scanning](https://arxiv.org/abs/2102.06120)
:star:[code](https://github.com/minhmanho/dpscan):house:[project](https://minhmanho.github.io/dpscan/)
* 图像增强
* [Learning Color Representations for Low-Light Image Enhancement](https://openaccess.thecvf.com/content/WACV2022/papers/Kim_Learning_Color_Representations_for_Low-Light_Image_Enhancement_WACV_2022_paper.pdf)
* 图像质量评估
* [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://arxiv.org/abs/2108.06858)
* Image reenactment(图像重演)
* [Single Source One Shot Reenactment using Weighted motion From Paired Feature Points](https://arxiv.org/abs/2104.03117)
* Image decomposition(图像分解)
* [Fast Nonlinear Image Unblending](https://openaccess.thecvf.com/content/WACV2022/papers/Horita_Fast_Nonlinear_Image_Unblending_WACV_2022_paper.pdf)
* HDR
* [High Dynamic Range Imaging of Dynamic Scenes With Saturation Compensation but Without Explicit Motion Compensation](https://openaccess.thecvf.com/content/WACV2022/papers/Chung_High_Dynamic_Range_Imaging_of_Dynamic_Scenes_With_Saturation_Compensation_WACV_2022_paper.pdf)
:star:[code](https://github.com/haesoochung/hdri-saturation-compensation)
* [Single-Photon Camera Guided Extreme Dynamic Range Imaging](https://openaccess.thecvf.com/content/WACV2022/papers/Liu_Single-Photon_Camera_Guided_Extreme_Dynamic_Range_Imaging_WACV_2022_paper.pdf)
* Auto white balance(自动白平衡)
* [Auto White-Balance Correction for Mixed-Illuminant Scenes](https://arxiv.org/abs/2109.08750)
:star:[code](https://github.com/mahmoudnafifi/mixedillWB)
## 7.Human Pose(人体姿态)
* 人体动作合成
* [Generative Adversarial Graph Convolutional Networks for Human Action Synthesis](https://arxiv.org/abs/2110.11191)
:star:[code](https://github.com/DegardinBruno/Kinetic-GAN)
* 3D人体
* [Matching and Recovering 3D People From Multiple Views](https://openaccess.thecvf.com/content/WACV2022/papers/Perez-Yus_Matching_and_Recovering_3D_People_From_Multiple_Views_WACV_2022_paper.pdf)
* 人体姿态估计
* [Transfer Learning for Pose Estimation of Illustrated Characters](https://arxiv.org/abs/2108.01819)
* [PERF-Net: Pose Empowered RGB-Flow Net](https://openaccess.thecvf.com/content/WACV2022/papers/Li_PERF-Net_Pose_Empowered_RGB-Flow_Net_WACV_2022_paper.pdf)
* [Bayesian Uncertainty and Expected Gradient Length - Regression: Two Sides of the Same Coin?](https://openaccess.thecvf.com/content/WACV2022/papers/Shukla_Bayesian_Uncertainty_and_Expected_Gradient_Length_-_Regression_Two_Sides_WACV_2022_paper.pdf)
:star:[code](https://github.com/meghshukla/ActiveLearningForHumanPose)
* [Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation](https://arxiv.org/abs/2110.11680)
* 3D人体姿态估计
* [PoP-Net: Pose Over Parts Network for Multi-Person 3D Pose Estimation From a Depth Image](https://openaccess.thecvf.com/content/WACV2022/papers/Guo_PoP-Net_Pose_Over_Parts_Network_for_Multi-Person_3D_Pose_Estimation_WACV_2022_paper.pdf)
:star:[code](https://github.com/oppo-us-research/PoP-Net)
* 3D手部姿势估计
* [Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation](https://arxiv.org/abs/2111.06500)
* 头部姿势估计
* [LwPosr: Lightweight Efficient Fine Grained Head Pose Estimation](https://openaccess.thecvf.com/content/WACV2022/papers/Dhingra_LwPosr_Lightweight_Efficient_Fine_Grained_Head_Pose_Estimation_WACV_2022_paper.pdf)
* [HHP-Net: A Light Heteroscedastic Neural Network for Head Pose Estimation With Uncertainty](https://openaccess.thecvf.com/content/WACV2022/papers/Cantarini_HHP-Net_A_Light_Heteroscedastic_Neural_Network_for_Head_Pose_Estimation_WACV_2022_paper.pdf)
:star:[code](https://github.com/cantarinigiorgio/HHP-Net)
* 三维人体模型
* [Creating and Reenacting Controllable 3D Humans with Differentiable Rendering](https://arxiv.org/abs/2110.11746)
* 人体形状
* [A Riemannian Framework for Analysis of Human Body Surface](https://arxiv.org/abs/2108.11449)
## 6.Video(视频相关)
* 无监督视频域适应
* [Multi-Level Attentive Adversarial Learning With Temporal Dilation for Unsupervised Video Domain Adaptation](https://openaccess.thecvf.com/content/WACV2022/papers/Chen_Multi-Level_Attentive_Adversarial_Learning_With_Temporal_Dilation_for_Unsupervised_Video_WACV_2022_paper.pdf)
* Partial Video Copy Detection(局部视频拷贝检测)
* [A Fast Partial Video Copy Detection Using KNN and Global Feature Database](https://arxiv.org/abs/2105.01713)
* 异常检测
* [Discrete Neural Representations for Explainable Anomaly Detection](https://arxiv.org/abs/2112.05585)
:house:[project](http://jjcvision.com/projects/vqunet_anomally_detection.html):tv:[video](https://youtu.be/3KLRi0biQvY)
* [Rethinking Video Anomaly Detection - A Continual Learning Approach](https://openaccess.thecvf.com/content/WACV2022/papers/Doshi_Rethinking_Video_Anomaly_Detection_-_A_Continual_Learning_Approach_WACV_2022_paper.pdf)
* [A Modular and Unified Framework for Detecting and Localizing Video Anomalies](https://arxiv.org/abs/2103.11299)
* [FastAno: Fast Anomaly Detection via Spatio-Temporal Patch Transformation](https://arxiv.org/abs/2106.08613)
* [Multi-Branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions](https://openaccess.thecvf.com/content/WACV2022/papers/Leroux_Multi-Branch_Neural_Networks_for_Video_Anomaly_Detection_in_Adverse_Lighting_WACV_2022_paper.pdf)
* sarcasm and humor detection(讽刺与幽默检测)
* [Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection](https://arxiv.org/abs/2110.10949)
* 视频表征学习
* [Hierarchically Decoupled Spatial-Temporal Contrast for Self-Supervised Video Representation Learning](https://arxiv.org/abs/2011.11261)
* [Self-Supervised Video Representation Learning With Cross-Stream Prototypical Contrasting](https://arxiv.org/abs/2106.10137)
:star:[code](https://github.com/martinetoering/ViCC)
* 视频字幕
* [Co-Segmentation Aided Two-Stream Architecture for Video Captioning](https://openaccess.thecvf.com/content/WACV2022/papers/Vaidya_Co-Segmentation_Aided_Two-Stream_Architecture_for_Video_Captioning_WACV_2022_paper.pdf)
* [Variational Stacked Local Attention Networks for Diverse Video Captioning](https://openaccess.thecvf.com/content/WACV2022/papers/Deb_Variational_Stacked_Local_Attention_Networks_for_Diverse_Video_Captioning_WACV_2022_paper.pdf)
* 视频人物定位
* [Extraction of Positional Player Data From Broadcast Soccer Videos](https://openaccess.thecvf.com/content/WACV2022/papers/Theiner_Extraction_of_Positional_Player_Data_From_Broadcast_Soccer_Videos_WACV_2022_paper.pdf)
* 视频稳定
* [Deep Online Fused Video Stabilization](https://arxiv.org/abs/2102.01279)
:star:[code](https://github.com/googleinterns/deep-stabilization):house:[project](https://zhmeishi.github.io/dvs/):tv:[video](https://youtu.be/LF_JVdUFIw8)
* 视频理解
* [Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search](https://openaccess.thecvf.com/content/WACV2022/papers/Jiang_Auto-X3D_Ultra-Efficient_Video_Understanding_via_Finer-Grained_Neural_Architecture_Search_WACV_2022_paper.pdf)
* 视频分类
* [Busy-Quiet Video Disentangling for Video Classification](https://arxiv.org/abs/2103.15584)
:star:[code](https://github.com/guoxih/busy-quiet-net)
* 视频摘要
* [Multi-Stream Dynamic Video Summarization](https://arxiv.org/abs/1812.00108)
* 有声视频合成
* [Strumming to the Beat: Audio-Conditioned Contrastive Video Textures](https://arxiv.org/abs/2104.02687)
:star:[code](https://github.com/medhini/audio-video-textures):house:[project](https://medhini.github.io/audio_video_textures/):tv:[video](https://youtu.be/JCuEbSF4kxU)
* 视频帧插值
* [Enhanced Correlation Matching Based Video Frame Interpolation](https://arxiv.org/abs/2111.08869)
* 视频时刻定位
* [Natural Language Video Moment Localization Through Query-Controlled Temporal Convolution](https://openaccess.thecvf.com/content/WACV2022/papers/Zhang_Natural_Language_Video_Moment_Localization_Through_Query-Controlled_Temporal_Convolution_WACV_2022_paper.pdf)
* Temporal Video Segmentation(时序视频分割)
* [Learning Temporal Video Procedure Segmentation From an Automatically Collected Large Dataset](https://openaccess.thecvf.com/content/WACV2022/papers/Ji_Learning_Temporal_Video_Procedure_Segmentation_From_an_Automatically_Collected_Large_WACV_2022_paper.pdf)
## 5.Object Detection(目标检测)
* [Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection Using Meta-Learning](https://openaccess.thecvf.com/content/WACV2022/papers/VS_Meta-UDA_Unsupervised_Domain_Adaptive_Thermal_Object_Detection_Using_Meta-Learning_WACV_2022_paper.pdf)
* [ADC: Adversarial Attacks Against Object Detection That Evade Context Consistency Checks](https://arxiv.org/abs/2110.12321)
* [TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection](https://arxiv.org/abs/2104.11435)
:star:[code](https://github.com/qjadud1994/TricubeNet)
* [Detecting Tear Gas Canisters With Limited Training Data](https://openaccess.thecvf.com/content/WACV2022/papers/DCruz_Detecting_Tear_Gas_Canisters_With_Limited_Training_Data_WACV_2022_paper.pdf)
* [Learned Event-Based Visual Perception for Improved Space Object Detection](https://openaccess.thecvf.com/content/WACV2022/papers/Salvatore_Learned_Event-Based_Visual_Perception_for_Improved_Space_Object_Detection_WACV_2022_paper.pdf)
* [Densely-Packed Object Detection via Hard Negative-Aware Anchor Attention](https://openaccess.thecvf.com/content/WACV2022/papers/Cho_Densely-Packed_Object_Detection_via_Hard_Negative-Aware_Anchor_Attention_WACV_2022_paper.pdf)
* [PICA: Point-Wise Instance and Centroid Alignment Based Few-Shot Domain Adaptive Object Detection With Loose Annotations](https://openaccess.thecvf.com/content/WACV2022/papers/Zhong_PICA_Point-Wise_Instance_and_Centroid_Alignment_Based_Few-Shot_Domain_Adaptive_WACV_2022_paper.pdf)
* [Improving Object Detection by Label Assignment Distillation](https://arxiv.org/abs/2108.10520)
:star:[code](https://github.com/cybercore-co-ltd/CoLAD)
* [Fusion Point Pruning for Optimized 2D Object Detection With Radar-Camera Fusion](https://openaccess.thecvf.com/content/WACV2022/papers/Stacker_Fusion_Point_Pruning_for_Optimized_2D_Object_Detection_With_Radar-Camera_WACV_2022_paper.pdf)
* [YOLO-ReT: Towards High Accuracy Real-Time Object Detection on Edge GPUs](https://openaccess.thecvf.com/content/WACV2022/papers/Ganesh_YOLO-ReT_Towards_High_Accuracy_Real-Time_Object_Detection_on_Edge_GPUs_WACV_2022_paper.pdf)
:star:[code](https://github.com/prakharg24/yoloret)
* [SC-UDA: Style and Content Gaps Aware Unsupervised Domain Adaptation for Object Detection](https://openaccess.thecvf.com/content/WACV2022/papers/Yu_SC-UDA_Style_and_Content_Gaps_Aware_Unsupervised_Domain_Adaptation_for_WACV_2022_paper.pdf)
* [To Miss-Attend Is to Misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors](https://openaccess.thecvf.com/content/WACV2022/papers/Khindkar_To_Miss-Attend_Is_to_Misalign_Residual_Self-Attentive_Feature_Alignment_for_WACV_2022_paper.pdf)
:star:[code](https://github.com/Vaishnvi/ILLUME)
* 目标定位
* 无监督目标定位
* [F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling](https://openaccess.thecvf.com/content/WACV2022/papers/Belharbi_F-CAM_Full_Resolution_Class_Activation_Maps_via_Guided_Parametric_Upscaling_WACV_2022_paper.pdf)
:star:[code](https://github.com/sbelharbi/fcam-wsol)
* MOD(移动目标检测)
* [Multi-Motion and Appearance Self-Supervised Moving Object Detection](https://openaccess.thecvf.com/content/WACV2022/papers/Yang_Multi-Motion_and_Appearance_Self-Supervised_Moving_Object_Detection_WACV_2022_paper.pdf)
* 路标检测
* [CeyMo: See More on Roads - A Novel Benchmark Dataset for Road Marking Detection](https://openaccess.thecvf.com/content/WACV2022/papers/Jayasinghe_CeyMo_See_More_on_Roads_-_A_Novel_Benchmark_Dataset_WACV_2022_paper.pdf)
:star:[code](https://github.com/oshadajay/CeyMo)
* 零样本检测
* [From Node To Graph: Joint Reasoning on Visual-Semantic Relational Graph for Zero-Shot Detection](https://openaccess.thecvf.com/content/WACV2022/papers/Nie_From_Node_To_Graph_Joint_Reasoning_on_Visual-Semantic_Relational_Graph_WACV_2022_paper.pdf)
* 小样本目标检测
* [Few-Shot Object Detection by Attending to Per-Sample-Prototype](https://arxiv.org/abs/2109.07734)
* 图像异常检测
* [Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Tsai_Multi-Scale_Patch-Based_Representation_Learning_for_Image_Anomaly_Detection_and_Segmentation_WACV_2022_paper.pdf)
* 弱监督目标检测
* [Few-Shot Weakly-Supervised Object Detection via Directional Statistics](https://arxiv.org/abs/2103.14162)
* 海上障碍物检测
* [Learning Maritime Obstacle Detection From Weak Annotations by Scaffolding](https://openaccess.thecvf.com/content/WACV2022/papers/Zust_Learning_Maritime_Obstacle_Detection_From_Weak_Annotations_by_Scaffolding_WACV_2022_paper.pdf)
* 人造卫星识别
* [SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery](https://openaccess.thecvf.com/content/WACV2022/papers/Gazak_SpectraNet_Learned_Recognition_of_Artificial_Satellites_From_High_Contrast_Spectroscopic_WACV_2022_paper.pdf)
* Object Anti-Spoofing
* [MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data](https://arxiv.org/abs/2110.04066)
:star:[code](https://github.com/SamsungSDS-Team9/mToFNet)
* 3D目标检测
* [ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection](https://arxiv.org/abs/2106.01178)
:star:[code](https://github.com/saic-vul/imvoxelnet)
* [Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection](https://openaccess.thecvf.com/content/WACV2022/papers/Pang_Fast-CLOCs_Fast_Camera-LiDAR_Object_Candidates_Fusion_for_3D_Object_Detection_WACV_2022_paper.pdf)
* [M3DETR: Multi-Representation, Multi-Scale, Mutual-Relation 3D Object Detection With Transformers](https://arxiv.org/abs/2104.11896)
:star:[code](https://github.com/rayguan97/M3DETR)
* 显著目标检测
* [Video Salient Object Detection via Contrastive Features and Attention Modules](https://arxiv.org/abs/2111.02368)
* [Recursive Contour-Saliency Blending Network for Accurate Salient Object Detection](https://arxiv.org/abs/2105.13865)
:star:[code](https://github.com/BarCodeReader/RCSB-PyTorch)
* 伪装目标检测
* [Modeling Aleatoric Uncertainty for Camouflaged Object Detection](https://openaccess.thecvf.com/content/WACV2022/papers/Liu_Modeling_Aleatoric_Uncertainty_for_Camouflaged_Object_Detection_WACV_2022_paper.pdf)
* 球员检测
* [Transductive Weakly-Supervised Player Detection Using Soccer Broadcast Videos](https://openaccess.thecvf.com/content/WACV2022/papers/Gadde_Transductive_Weakly-Supervised_Player_Detection_Using_Soccer_Broadcast_Videos_WACV_2022_paper.pdf)
* Wireframe Detection(线框检测)
* [Hole-robust Wireframe Detection](https://arxiv.org/abs/2111.15064)
## 4.GAN(生成对抗网络)
* [GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks](https://openaccess.thecvf.com/content/WACV2022/papers/Bhaskara_GraN-GAN_Piecewise_Gradient_Normalization_for_Generative_Adversarial_Networks_WACV_2022_paper.pdf)
* [Latent to Latent: A Learned Mapper for Identity Preserving Editing of Multiple Face Attributes in StyleGAN-Generated Images](https://openaccess.thecvf.com/content/WACV2022/papers/Khodadadeh_Latent_to_Latent_A_Learned_Mapper_for_Identity_Preserving_Editing_WACV_2022_paper.pdf)
* [AE-StyleGAN: Improved Training of Style-Based Auto-Encoders](https://openaccess.thecvf.com/content/WACV2022/papers/Han_AE-StyleGAN_Improved_Training_of_Style-Based_Auto-Encoders_WACV_2022_paper.pdf)
:star:[code](https://github.com/phymhan/stylegan2-pytorch)
* [GANs Spatial Control via Inference-Time Adaptive Normalization](https://openaccess.thecvf.com/content/WACV2022/papers/Jakoel_GANs_Spatial_Control_via_Inference-Time_Adaptive_Normalization_WACV_2022_paper.pdf)
* [Latent Reweighting, an Almost Free Improvement for GANs](https://arxiv.org/abs/2110.09803)
* [PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression](https://openaccess.thecvf.com/content/WACV2022/papers/Vo_PPCD-GAN_Progressive_Pruning_and_Class-Aware_Distillation_for_Large-Scale_Conditional_GANs_WACV_2022_paper.pdf)
* [Controlled GAN-Based Creature Synthesis via a Challenging Game Art Dataset - Addressing the Noise-Latent Trade-Off](https://openaccess.thecvf.com/content/WACV2022/papers/Vavilala_Controlled_GAN-Based_Creature_Synthesis_via_a_Challenging_Game_Art_Dataset_WACV_2022_paper.pdf)
* [Data InStance Prior (DISP) in Generative Adversarial Networks](https://arxiv.org/abs/2012.04256)
* Sketch-To-Face草图到人脸图像翻译
* [S2FGAN: Semantically Aware Interactive Sketch-To-Face Translation](https://arxiv.org/abs/2011.14785)
:star:[code](https://github.com/Yan98/S2FGAN)
* 基于关键点重新合成新姿势
* [CharacterGAN: Few-Shot Keypoint Character Animation and Reposing](https://arxiv.org/abs/2102.03141)
:star:[code](https://github.com/tohinz/CharacterGAN)
* MRI重建
* [Compressed Sensing MRI Reconstruction With Co-VeGAN: Complex-Valued Generative Adversarial Network](https://openaccess.thecvf.com/content/WACV2022/papers/Vasudeva_Compressed_Sensing_MRI_Reconstruction_With_Co-VeGAN_Complex-Valued_Generative_Adversarial_Network_WACV_2022_paper.pdf)
:star:[code](https://github.com/estija/Co-VeGAN)
## 3.3D(三维视觉)
* 深度估计
* [SIDE: Center-Based Stereo 3D Detector With Structure-Aware Instance Depth Estimation](https://arxiv.org/abs/2108.09663)
* [Estimating Image Depth in the Comics Domain](https://arxiv.org/abs/2110.03575)
* [Self-Supervised Learning of Domain Invariant Features for Depth Estimation](https://arxiv.org/abs/2106.02594)
* 单目深度估计
* [Monocular Depth Estimation With Adaptive Geometric Attention](https://openaccess.thecvf.com/content/WACV2022/papers/Naderi_Monocular_Depth_Estimation_With_Adaptive_Geometric_Attention_WACV_2022_paper.pdf)
* [EdgeConv With Attention Module for Monocular Depth Estimation](https://arxiv.org/abs/2106.08615)
* stereo images
* [SBEVNet: End-to-End Deep Stereo Layout Estimation](https://arxiv.org/abs/2105.11705)
:star:[code](https://github.com/divamgupta/sbevnet-stereo-layout-estimation)
* [MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching](https://arxiv.org/abs/2108.09770)
:star:[code](https://github.com/cogsys-tuebingen/mobilestereonet)
* 三维重建
* [Single-Shot Dense Active Stereo With Pixel-Wise Phase Estimation Based on Grid-Structure Using CNN and Correspondence Estimation Using GCN](https://openaccess.thecvf.com/content/WACV2022/papers/Furukawa_Single-Shot_Dense_Active_Stereo_With_Pixel-Wise_Phase_Estimation_Based_on_WACV_2022_paper.pdf)
* [Style Agnostic 3D Reconstruction via Adversarial Style Transfer](https://arxiv.org/abs/2110.10784)
:star:[code](https://github.com/Felix-Petersen/style-agnostic-3d-reconstruction)
* [3D Modeling Beneath Ground: Plant Root Detection and Reconstruction Based on Ground-Penetrating Radar](https://openaccess.thecvf.com/content/WACV2022/papers/Lu_3D_Modeling_Beneath_Ground_Plant_Root_Detection_and_Reconstruction_Based_WACV_2022_paper.pdf)
* [Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild](https://arxiv.org/abs/2101.06860)
* [Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image](https://arxiv.org/abs/2104.07986)
:star:[code](https://github.com/Cyang0515/NonCuboidRoom)
* [Tensor-Based Non-Rigid Structure From Motion](https://openaccess.thecvf.com/content/WACV2022/papers/Grasshof_Tensor-Based_Non-Rigid_Structure_From_Motion_WACV_2022_paper.pdf)
* stereo vision(立体视觉)
* [PredStereo: An Accurate Real-Time Stereo Vision System](https://openaccess.thecvf.com/content/WACV2022/papers/Moolchandani_PredStereo_An_Accurate_Real-Time_Stereo_Vision_System_WACV_2022_paper.pdf)
* 网格重建
* [AttWalk: Attentive Cross-Walks for Deep Mesh Analysis](https://arxiv.org/abs/2104.11571)
## 2.Medical Image(医学影像)
* 分割
* [UNETR: Transformers for 3D Medical Image Segmentation](https://arxiv.org/abs/2103.10504)
:star:[code](https://github.com/Project-MONAI/research-contributions/tree/master/UNETR)
* [Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation](https://arxiv.org/abs/2110.02117)
:star:[code](https://github.com/devavratTomar/SST)
* [AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Yan_AFTer-UNet_Axial_Fusion_Transformer_UNet_for_Medical_Image_Segmentation_WACV_2022_paper.pdf)
* [Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Liu_Co-Net_A_Collaborative_Region-Contour-Driven_Network_for_Fine-to-Finer_Medical_Image_Segmentation_WACV_2022_paper.pdf)
* [T-Net: A Resource-Constrained Tiny Convolutional Neural Network for Medical Image Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Khan_T-Net_A_Resource-Constrained_Tiny_Convolutional_Neural_Network_for_Medical_Image_WACV_2022_paper.pdf)
* [Hyper-Convolution Networks for Biomedical Image Segmentation](https://arxiv.org/abs/2105.10559)
:star:[code](https://github.com/tym002/Hyper-Convolution)
* 血管分割
* [Weakly-Supervised Convolutional Neural Networks for Vessel Segmentation in Cerebral Angiography](https://openaccess.thecvf.com/content/WACV2022/papers/Vepa_Weakly-Supervised_Convolutional_Neural_Networks_for_Vessel_Segmentation_in_Cerebral_Angiography_WACV_2022_paper.pdf)
* 腺体分割
* [TA-Net: Topology-Aware Network for Gland Segmentation](https://openaccess.thecvf.com/content/WACV2022/papers/Wang_TA-Net_Topology-Aware_Network_for_Gland_Segmentation_WACV_2022_paper.pdf)
* 检索
* [X-MIR: EXplainable Medical Image Retrieval](https://openaccess.thecvf.com/content/WACV2022/papers/Hu_X-MIR_EXplainable_Medical_Image_Retrieval_WACV_2022_paper.pdf)
:star:[code](https://gitlab.kitware.com/brianhhu/x-mir)
* 配准
* [Uncertainty Learning Towards Unsupervised Deformable Medical Image Registration](https://openaccess.thecvf.com/content/WACV2022/papers/Gong_Uncertainty_Learning_Towards_Unsupervised_Deformable_Medical_Image_Registration_WACV_2022_paper.pdf)
* 分类
* [Weakly Supervised Branch Network With Template Mask for Classifying Masses in 3D Automated Breast Ultrasound](https://openaccess.thecvf.com/content/WACV2022/papers/Kim_Weakly_Supervised_Branch_Network_With_Template_Mask_for_Classifying_Masses_WACV_2022_paper.pdf)
* 自动生成医学报告
* [Non-Local Attention Improves Description Generation for Retinal Images](https://openaccess.thecvf.com/content/WACV2022/papers/Huang_Non-Local_Attention_Improves_Description_Generation_for_Retinal_Images_WACV_2022_paper.pdf)
:star:[code](https://github.com/Jhhuangkay/Non-local-Attention-Improves-Description-Generation-for-Retinal-Images)
* 手术器械定位
* [Dynamic CNNs Using Uncertainty To Overcome Domain Generalization for Surgical Instrument Localization](https://openaccess.thecvf.com/content/WACV2022/papers/Philipp_Dynamic_CNNs_Using_Uncertainty_To_Overcome_Domain_Generalization_for_Surgical_WACV_2022_paper.pdf)
* 胸部X光片的异常分类和定位
* [Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-Rays With Radiomics Using a Feedback Loop](https://openaccess.thecvf.com/content/WACV2022/papers/Han_Knowledge-Augmented_Contrastive_Learning_for_Abnormality_Classification_and_Localization_in_Chest_WACV_2022_paper.pdf)
## 1.其它
* [Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias](https://arxiv.org/abs/2110.10389)
:star:[code](https://github.com/sumanyumuku98/contextual-bias)
* [The Untapped Potential of Off-the-Shelf Convolutional Neural Networks](https://arxiv.org/abs/2103.09891)
* [Unveiling Real-Life Effects of Online Photo Sharing](https://openaccess.thecvf.com/content/WACV2022/papers/Nguyen_Unveiling_Real-Life_Effects_of_Online_Photo_Sharing_WACV_2022_paper.pdf)
* [Shadow Art Revisited: A Differentiable Rendering Based Approach](https://arxiv.org/abs/2107.14539)
* [Towards Class-Oriented Poisoning Attacks Against Neural Networks](https://arxiv.org/abs/2008.00047)
* [Predicting Levels of Household Electricity Consumption in Low-Access Settings](https://arxiv.org/abs/2112.08497)
* [Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo](https://arxiv.org/abs/2110.05594)
* [PRECODE - A Generic Model Extension To Prevent Deep Gradient Leakage](https://openaccess.thecvf.com/content/WACV2022/papers/Scheliga_PRECODE_-_A_Generic_Model_Extension_To_Prevent_Deep_Gradient_WACV_2022_paper.pdf)
* [Discovering Underground Maps From Fashion](https://arxiv.org/abs/2012.02897)
* [On the Maximum Radius of Polynomial Lens Distortion](https://openaccess.thecvf.com/content/WACV2022/papers/Leotta_On_the_Maximum_Radius_of_Polynomial_Lens_Distortion_WACV_2022_paper.pdf)
:star:[code](https://github.com/Kitware/max-lens-radius)
* [The Hitchhiker's Guide to Prior-Shift Adaptation](https://openaccess.thecvf.com/content/WACV2022/papers/Sipka_The_Hitchhikers_Guide_to_Prior-Shift_Adaptation_WACV_2022_paper.pdf)
:star:[code](https://github.com/sipkatom/The-Hitchhiker-s-Guide-to-Prior-Shift-Adaptation)
* [FalCon: Fine-Grained Feature Map Sparsity Computing With Decomposed Convolutions for Inference Optimization](https://openaccess.thecvf.com/content/WACV2022/papers/Xu_FalCon_Fine-Grained_Feature_Map_Sparsity_Computing_With_Decomposed_Convolutions_for_WACV_2022_paper.pdf)
* [METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy](https://arxiv.org/abs/2104.10993)
* [Agree To Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations](https://arxiv.org/abs/2105.06791)
:star:[code](https://github.com/mattswatson/agree-to-disagree)
* [REFICS: A Step Towards Linking Vision With Hardware Assurance](https://openaccess.thecvf.com/content/WACV2022/papers/Wilson_REFICS_A_Step_Towards_Linking_Vision_With_Hardware_Assurance_WACV_2022_paper.pdf)
* [Deep Optimization Prior for THz Model Parameter Estimation](https://openaccess.thecvf.com/content/WACV2022/papers/Wong_Deep_Optimization_Prior_for_THz_Model_Parameter_Estimation_WACV_2022_paper.pdf)
* [Sharing Decoders: Network Fission for Multi-Task Pixel Prediction](https://openaccess.thecvf.com/content/WACV2022/papers/Hickson_Sharing_Decoders_Network_Fission_for_Multi-Task_Pixel_Prediction_WACV_2022_paper.pdf)
* [Fair Visual Recognition in Limited Data Regime using Self-Supervision and Self-Distillation](https://arxiv.org/abs/2107.00067)
* [Low-Cost Multispectral Scene Analysis With Modality Distillation](https://openaccess.thecvf.com/content/WACV2022/papers/Zhang_Low-Cost_Multispectral_Scene_Analysis_With_Modality_Distillation_WACV_2022_paper.pdf)
* [Self-Supervised Pretraining Improves Self-Supervised Pretraining](https://arxiv.org/abs/2103.12718)
* [PROVES: Establishing Image Provenance Using Semantic Signatures](https://arxiv.org/abs/2110.11411)
* [Addressing Out-of-Distribution Label Noise in Webly-Labelled Data](https://arxiv.org/abs/2110.13699)
:star:[code](https://github.com/PaulAlbert31/DSOS)
* [Towards Durability Estimation of Bioprosthetic Heart Valves via Motion Symmetry Analysis](https://openaccess.thecvf.com/content/WACV2022/papers/Alizadeh_Towards_Durability_Estimation_of_Bioprosthetic_Heart_Valves_via_Motion_Symmetry_WACV_2022_paper.pdf)
* [Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains](https://arxiv.org/abs/2108.07399)
* [Generalized Clustering and Multi-Manifold Learning With Geometric Structure Preservation](https://arxiv.org/abs/2009.09590)
:star:[code](https://github.com/LirongWu/GCML)
* [Batch Normalization Tells You Which Filter Is Important](https://arxiv.org/abs/2112.01155)
* [Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity](https://arxiv.org/abs/2102.11382)
:star:[code](https://github.com/VITA-Group/Sandwich-Batch-Normalizationhttps://arxiv.org/abs/2102.11382)
* [Parsing Line Chart Images Using Linear Programming](https://openaccess.thecvf.com/content/WACV2022/papers/Kato_Parsing_Line_Chart_Images_Using_Linear_Programming_WACV_2022_paper.pdf)
* [CrossLocate: Cross-Modal Large-Scale Visual Geo-Localization in Natural Environments Using Rendered Modalities](https://openaccess.thecvf.com/content/WACV2022/papers/Tomesek_CrossLocate_Cross-Modal_Large-Scale_Visual_Geo-Localization_in_Natural_Environments_Using_Rendered_WACV_2022_paper.pdf)
:house:[project](http://cphoto.fit.vutbr.cz/crosslocate/)
* [Symmetric-Light Photometric Stereo](https://openaccess.thecvf.com/content/WACV2022/papers/Minami_Symmetric-Light_Photometric_Stereo_WACV_2022_paper.pdf)
* [REGroup: Rank-Aggregating Ensemble of Generative Classifiers for Robust Predictions](https://arxiv.org/abs/2006.10679)
:house:[project](https://lokender.github.io/REGroup.html):star:[code](https://github.com/lokender/REGroup)
* [Leveraging Test-Time Consensus Prediction for Robustness Against Unseen Noise](https://openaccess.thecvf.com/content/WACV2022/papers/Sarkar_Leveraging_Test-Time_Consensus_Prediction_for_Robustness_Against_Unseen_Noise_WACV_2022_paper.pdf)
* [Supervised Compression for Resource-Constrained Edge Computing Systems](https://arxiv.org/abs/2108.11898)
:star:[code](https://github.com/yoshitomo-matsubara/supervised-compression)
* [Action Anticipation Using Latent Goal Learning](https://openaccess.thecvf.com/content/WACV2022/papers/Roy_Action_Anticipation_Using_Latent_Goal_Learning_WACV_2022_paper.pdf)
:star:[code](https://github.com/debadityaroy/LatentGoal)
* [Non-Semantic Evaluation of Image Forensics Tools: Methodology and Database](https://arxiv.org/abs/2105.02700)
* [Inpaint2Learn: A Self-Supervised Framework for Affordance Learning](https://openaccess.thecvf.com/content/WACV2022/papers/Zhang_Inpaint2Learn_A_Self-Supervised_Framework_for_Affordance_Learning_WACV_2022_paper.pdf)
* [RGL-NET: A Recurrent Graph Learning Framework for Progressive Part Assembly](https://openaccess.thecvf.com/content/WACV2022/papers/Narayan_RGL-NET_A_Recurrent_Graph_Learning_Framework_for_Progressive_Part_Assembly_WACV_2022_paper.pdf)
* [Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks](https://arxiv.org/abs/2110.12696)
:star:[code](https://github.com/generation21/Self-Supervised-Knowledge-Transfer-via-Loosely-Supervised-Auxiliary-Tasks)
* [Novel Ensemble Diversification Methods for Open-Set Scenarios](https://openaccess.thecvf.com/content/WACV2022/papers/Farber_Novel_Ensemble_Diversification_Methods_for_Open-Set_Scenarios_WACV_2022_paper.pdf)
* [Contrast To Divide: Self-Supervised Pre-Training for Learning With Noisy Labels](https://arxiv.org/abs/2103.13646)
:star:[code](https://github.com/ContrastToDivide/C2D)
* [Typenet: Towards Camera Enabled Touch Typing on Flat Surfaces Through Self-Refinement](https://openaccess.thecvf.com/content/WACV2022/papers/Maman_Typenet_Towards_Camera_Enabled_Touch_Typing_on_Flat_Surfaces_Through_WACV_2022_paper.pdf)
:star:[code](https://github.com/benadar293/typeNet)
* [Nonnegative Low-Rank Tensor Completion via Dual Formulation With Applications to Image and Video Completion](https://openaccess.thecvf.com/content/WACV2022/papers/Sinha_Nonnegative_Low-Rank_Tensor_Completion_via_Dual_Formulation_With_Applications_to_WACV_2022_paper.pdf)
* [MisConv: Convolutional Neural Networks for Missing Data](https://openaccess.thecvf.com/content/WACV2022/papers/Przewiezlikowski_MisConv_Convolutional_Neural_Networks_for_Missing_Data_WACV_2022_paper.pdf)
* [MAPS: Multimodal Attention for Product Similarity](https://openaccess.thecvf.com/content/WACV2022/papers/Das_MAPS_Multimodal_Attention_for_Product_Similarity_WACV_2022_paper.pdf)
* [Global Assists Local: Effective Aerial Representations for Field of View Constrained Image Geo-Localization](https://openaccess.thecvf.com/content/WACV2022/papers/Rodrigues_Global_Assists_Local_Effective_Aerial_Representations_for_Field_of_View_WACV_2022_paper.pdf)
* [Self-Supervised Test-Time Adaptation on Video Data](https://openaccess.thecvf.com/content/WACV2022/papers/Azimi_Self-Supervised_Test-Time_Adaptation_on_Video_Data_WACV_2022_paper.pdf)
* [FT-DeepNets: Fault-Tolerant Convolutional Neural Networks With Kernel-Based Duplication](https://openaccess.thecvf.com/content/WACV2022/papers/Baek_FT-DeepNets_Fault-Tolerant_Convolutional_Neural_Networks_With_Kernel-Based_Duplication_WACV_2022_paper.pdf)
* [Short-Term Solar Irradiance Prediction From Sky Images With a Clear Sky Model](https://openaccess.thecvf.com/content/WACV2022/papers/Gao_Short-Term_Solar_Irradiance_Prediction_From_Sky_Images_With_a_Clear_WACV_2022_paper.pdf)
* [Reconstructing Training Data From Diverse ML Models by Ensemble Inversion](https://arxiv.org/abs/2111.03702)
* [How Good Is Your Explanation? Algorithmic Stability Measures To Assess the Quality of Explanations for Deep Neural Networks](https://openaccess.thecvf.com/content/WACV2022/papers/Fel_How_Good_Is_Your_Explanation_Algorithmic_Stability_Measures_To_Assess_WACV_2022_paper.pdf)
* [Seeing Implicit Neural Representations As Fourier Series](https://openaccess.thecvf.com/content/WACV2022/papers/Benbarka_Seeing_Implicit_Neural_Representations_As_Fourier_Series_WACV_2022_paper.pdf)
* [Human-Aided Saliency Maps Improve Generalization of Deep Learning](https://arxiv.org/abs/2105.03492)
* [Cross-Modal Adversarial Reprogramming](https://arxiv.org/abs/2102.07325)
* [Learning From the CNN-Based Compressed Domain](https://openaccess.thecvf.com/content/WACV2022/papers/Wang_Learning_From_the_CNN-Based_Compressed_Domain_WACV_2022_paper.pdf)
* [Spatiotemporal Initialization for 3D CNNs With Generated Motion Patterns](https://openaccess.thecvf.com/content/WACV2022/papers/Kataoka_Spatiotemporal_Initialization_for_3D_CNNs_With_Generated_Motion_Patterns_WACV_2022_paper.pdf)
:house:[project](https://hirokatsukataoka16.github.io/Spatiotemporal-Initialization-for-3DCNNs/)
* [DAD: Data-Free Adversarial Defense at Test Time](https://openaccess.thecvf.com/content/WACV2022/papers/Nayak_DAD_Data-Free_Adversarial_Defense_at_Test_Time_WACV_2022_paper.pdf)
* [Geometry-Inspired Top-K Adversarial Perturbations](https://openaccess.thecvf.com/content/WACV2022/papers/Tursynbek_Geometry-Inspired_Top-K_Adversarial_Perturbations_WACV_2022_paper.pdf)
* [Shape-Coded ArUco: Fiducial Marker for Bridging 2D and 3D Modalities](https://openaccess.thecvf.com/content/WACV2022/papers/Makabe_Shape-Coded_ArUco_Fiducial_Marker_for_Bridging_2D_and_3D_Modalities_WACV_2022_paper.pdf)
* [Interpretable Semantic Photo Geolocation](https://openaccess.thecvf.com/content/WACV2022/papers/Theiner_Interpretable_Semantic_Photo_Geolocation_WACV_2022_paper.pdf)
:star:[code](https://github.com/jtheiner/semantic_geo_partitioning)
* [Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes](https://arxiv.org/abs/2110.09401)
:star:[code](https://github.com/Fraunhofer-SCAI/conv_sr_mesh_autoencoder)
* [Geometry-Aware Hierarchical Bayesian Learning on Manifolds](https://arxiv.org/abs/2111.00184)
* [Transferable 3D Adversarial Textures Using End-to-End Optimization](https://openaccess.thecvf.com/content/WACV2022/papers/Pestana_Transferable_3D_Adversarial_Textures_Using_End-to-End_Optimization_WACV_2022_paper.pdf)
* [Improving Fractal Pre-Training](https://arxiv.org/abs/2110.03091)
:star:[code](https://github.com/catalys1/fractal-pretraining):house:[project](https://catalys1.github.io/fractal-pretraining/)