Papers with Code 아카이브
Action Detection
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Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IIT-PAVIS/subspace-clustering-action-recognition
공식 코드Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
https://github.com/IoBT-VISTEC/SleepPoseNet
공식 코드SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB
https://github.com/JHang2020/HiCLR
공식 코드https://github.com/JHang2020/Shap-Mix
공식 코드Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition
https://github.com/Jho-Yonsei/HD-GCN
공식 코드Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Jho-Yonsei/STC-Net
공식 코드Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
https://github.com/JunweiLiang/Multiverse
공식 코드From Recognition to Prediction: Analysis of Human Action and Trajectory Prediction in Video
https://github.com/KAIST-VICLab/SkateFormer
공식 코드SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition
https://github.com/KAIST-VICLab/TDSM
공식 코드TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
https://github.com/KennCoder7/RAN
공식 코드https://github.com/LanglandsLin/MS2L
공식 코드MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
https://github.com/LeiWangR/HDG
공식 코드A Comparative Review of Recent Kinect-based Action Recognition Algorithms
https://github.com/Levigty/CMCS
공식 코드Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
https://github.com/Lou1sM/HAR
공식 코드Efficient Deep Clustering of Human Activities and How to Improve Evaluation
https://github.com/MCG-NJU/CRCNN-Action
공식 코드Context-Aware RCNN: A Baseline for Action Detection in Videos
https://github.com/MCG-NJU/MOC-Detector
공식 코드https://github.com/MCG-NJU/MultiSports
공식 코드MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions
https://github.com/MCG-NJU/RTD-Action
공식 코드Relaxed Transformer Decoders for Direct Action Proposal Generation
https://github.com/MVIG-SJTU/AlphAction
공식 코드https://github.com/Maghoumi/DeepGRU
공식 코드https://github.com/Media-Smart/vedatad
공식 코드https://github.com/MichiganNLP/vlog_action_recognition
공식 코드https://github.com/Mikexu007/PCRP
공식 코드Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
https://github.com/Mvrjustid/IMWUT-Hierarchical-HAR-PBD
공식 코드Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data
https://github.com/Necolizer/CHASE
공식 코드CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
https://github.com/Necolizer/ISTA-Net
공식 코드https://github.com/NickWilkinson37/voxseg
공식 코드https://github.com/Ohav/rogue-agents
공식 코드https://github.com/OrdinaryQin/TCA-GCN
공식 코드Skeleton-based Action Recognition via Temporal-Channel Aggregation
https://github.com/Oshana/ESWA
공식 코드A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring
https://github.com/OxWearables/ssl-wearables
공식 코드Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/video/models/bmn
공식 코드BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/PetarV-/GAT
공식 코드https://github.com/RBurchard/hd-epic-tap
공식 코드Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset
https://github.com/RanaMostafaAbdElMohsen/Human_Activity_Recognition_using_Wearable_Sensors_Review_Challenges_Evaluation_Benchmark
공식 코드Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark
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