https://github.com/Jho-Yonsei/HD-GCN
공식 코드Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Jho-Yonsei/STC-Net
공식 코드Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
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/KaiyangZhou/Dassl.pytorch
공식 코드https://github.com/KennCoder7/RAN
공식 코드https://github.com/KimManjin/RSA
공식 코드Relational Self-Attention: What's Missing in Attention for Video Understanding
https://github.com/Kwanss/PCLNet
공식 코드Unsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTM
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/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/LinguoLi/CrosSCLR
공식 코드3D Human Action Representation Learning via Cross-View Consistency Pursuit
https://github.com/Lou1sM/HAR
공식 코드Efficient Deep Clustering of Human Activities and How to Improve Evaluation
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/CPD-Video
공식 코드Learning Spatiotemporal Features via Video and Text Pair Discrimination
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/TDN
공식 코드TDN: Temporal Difference Networks for Efficient Action Recognition
https://github.com/MCG-NJU/VideoMAE
공식 코드VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MCG-NJU/VideoMAE-Action-Detection
공식 코드VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
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/MTLab/MorphMLP
공식 코드MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
https://github.com/Maddy12/ActionRecognitionRobustnessEval
공식 코드Large-scale Robustness Analysis of Video Action Recognition Models
https://github.com/Maghoumi/DeepGRU
공식 코드https://github.com/ManifoldRG/MultiNet
공식 코드Benchmarking Vision, Language, & Action Models on Robotic Learning Tasks
https://github.com/Mathux/ACTOR
공식 코드Action-Conditioned 3D Human Motion Synthesis with Transformer VAE
https://github.com/MehwishG/T3D-CNN
공식 코드Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework
https://github.com/MichiganCOG/M-PACT
공식 코드M-PACT: An Open Source Platform for Repeatable Activity Classification Research
https://github.com/MichiganCOG/Surgical_Hands_RELEASE
공식 코드Temporally Guided Articulated Hand Pose Tracking in Surgical Videos
https://github.com/Mikexu007/AS-CAL
공식 코드https://github.com/Mikexu007/AS_CAL
공식 코드https://github.com/Mikexu007/PCRP
공식 코드Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
https://github.com/MohsenFayyaz89/T3D
공식 코드Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.