https://github.com/IkeaASM/IKEA_ASM_Dataset
공식 코드The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
Papers with Code 아카이브
Action Localization
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Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/JerryYLi/Dataset-REPAIR
공식 코드https://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/JonnyS1226/Ego4d_mq_3rd_solution
공식 코드ReLER@ZJU Submission to the Ego4D Moment Queries Challenge 2022
https://github.com/JunweiLiang/Multiverse
공식 코드The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
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/Kalana304/KORSAL
공식 코드KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action Localization
https://github.com/Khrylx/DLow
공식 코드DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
https://github.com/KimManjin/RSA
공식 코드Relational Self-Attention: What's Missing in Attention for Video Understanding
https://github.com/L-KID/Full-Video-Action-Recognition
공식 코드https://github.com/LanglandsLin/MS2L
공식 코드MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
https://github.com/LeYangNwpu/A2Net
공식 코드Revisiting Anchor Mechanisms for Temporal Action Localization
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/Lyz1213/BadEdit
공식 코드https://github.com/MCG-NJU/AWT
공식 코드AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation
https://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/MIT-HAN-LAB/temporal-shift-module
공식 코드TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/MMKE-Bench-ICLR/MMKE-Bench
공식 코드MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
https://github.com/Maddy12/ActionRecognitionRobustnessEval
공식 코드Large-scale Robustness Analysis of Video Action Recognition Models
https://github.com/Maghoumi/DeepGRU
공식 코드https://github.com/MarcusOlivecrona/REINVENT
공식 코드Molecular De Novo Design through Deep Reinforcement Learning
https://github.com/Media-Smart/vedatad
공식 코드https://github.com/Mikexu007/PCRP
공식 코드Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
https://github.com/MohsenFayyaz89/SCT
공식 코드SCT: Set Constrained Temporal Transformer for Set Supervised Action Segmentation
https://github.com/MohsenFayyaz89/T3D
공식 코드Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
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/OpenGVLab/VideoMAEv2
공식 코드VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
https://github.com/OrdinaryQin/TCA-GCN
공식 코드Skeleton-based Action Recognition via Temporal-Channel Aggregation
https://github.com/PIC4SeRCentre/MPOSE2021_Dataset
공식 코드Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.