https://github.com/hulianyuyy/STGAT
공식 코드Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition
Papers with Code 아카이브
Skeleton Based Action Recognition
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Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.
<span style="color:grey; opacity: 0.6">( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ideal-idea/sap
공식 코드https://github.com/iesymiao/cd-gcn
공식 코드A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition
https://github.com/iit-pavis/uhar_skeletal_laplacian
공식 코드https://github.com/jackyjsy/CVPR21Chal-SLR
공식 코드https://github.com/jackyjsy/sam-slr-v2
공식 코드Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble
https://github.com/jingyaojade/SpSt-GCN
공식 코드Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution
https://github.com/kaai520/PGFA
공식 코드Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature Alignment
https://github.com/kalpitthakkar/pb-gcn
공식 코드Part-based Graph Convolutional Network for Action Recognition
https://github.com/kathpra/lshr_lsht
공식 코드Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition
https://github.com/kennymckormick/pyskl
공식 코드PYSKL: Towards Good Practices for Skeleton Action Recognition
https://github.com/kenziyuliu/ms-g3d
공식 코드Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
https://github.com/kpeng9510/os-sar
공식 코드Navigating Open Set Scenarios for Skeleton-based Action Recognition
https://github.com/kpeng9510/proformer
공식 코드Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
https://github.com/kpeng9510/trans4soar
공식 코드Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
https://github.com/leiwangr/video-ar
공식 코드https://github.com/levigty/acl
공식 코드Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
https://github.com/levigty/aimclr
공식 코드https://github.com/limaosen0/AS-GCN
공식 코드Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
https://github.com/liujf69/MMCL-Action
공식 코드Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
https://github.com/liujf69/icmew2024-track10
공식 코드HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
https://github.com/lizaowo/hpi-gcn
공식 코드High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/lshiwjx/2s-AGCN
공식 코드Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
https://github.com/lshiwjx/DSTA-Net
공식 코드Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
https://github.com/lukashedegaard/continual-skeletons
공식 코드https://github.com/maclong01/STEP-CATFormer
공식 코드https://github.com/maoyunyao/mamp
공식 코드Masked Motion Predictors are Strong 3D Action Representation Learners
https://github.com/martinxm/gap
공식 코드Generative Action Description Prompts for Skeleton-based Action Recognition
https://github.com/martinxm/lst
공식 코드Generative Action Description Prompts for Skeleton-based Action Recognition
https://github.com/mdeff/cnn_graph
공식 코드Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/mduhme/fusion-gcn
공식 코드Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks
https://github.com/metrics-lab/st-fmri
공식 코드Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
https://github.com/microsoft/SGN
공식 코드Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
https://github.com/microsoft/View-Adaptive-Neural-Networks-for-Skeleton-based-Human-Action-Recognition
공식 코드https://github.com/ml-postech/skeleton-anonymization
공식 코드https://github.com/moonlight52137/st-vgcn
공식 코드Improving Skeleton-based Action Recognition with Interactive Object Information
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.