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Skeleton Based Action Recognition

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평가표: 34
보존된 평가 기록: 415

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/1xbq1/a2mc

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Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning

https://github.com/1xbq1/psp-learning

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Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition

https://github.com/ActionR-G/ActionHSIC

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Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion

https://github.com/BUPTSJZhang/SiT-MLP

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SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition

https://github.com/Chiaraplizz/ST-TR

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Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks

https://github.com/EHZ9NIWI7/MSF-GZSSAR

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Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition

https://github.com/Gbouna/RE-TCN

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Action Recognition in Real-World Ambient Assisted Living Environment

https://github.com/HuCui2022/DSTSA-GCN_Gesture

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DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling

https://github.com/IIT-PAVIS/subspace-clustering-action-recognition

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Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning

https://github.com/JHang2020/HiCLR

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Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations

https://github.com/JHang2020/Shap-Mix

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Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition

https://github.com/Jho-Yonsei/HD-GCN

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Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/Jho-Yonsei/STC-Net

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Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition

https://github.com/KAIST-VICLab/SkateFormer

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SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition

https://github.com/KAIST-VICLab/TDSM

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TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition

https://github.com/LanglandsLin/MS2L

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MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition

https://github.com/LeiWangR/HDG

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A Comparative Review of Recent Kinect-based Action Recognition Algorithms

https://github.com/Levigty/CMCS

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Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition

https://github.com/Maghoumi/DeepGRU

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DeepGRU: Deep Gesture Recognition Utility

https://github.com/Mikexu007/PCRP

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Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition

https://github.com/Necolizer/CHASE

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CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition

https://github.com/Necolizer/ISTA-Net

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Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition

https://github.com/OrdinaryQin/TCA-GCN

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Skeleton-based Action Recognition via Temporal-Channel Aggregation

https://github.com/PetarV-/GAT

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Graph Attention Networks

https://github.com/Sunnydreamrain/IndRNN_Theano_Lasagne

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Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN

https://github.com/Sunnydreamrain/IndRNN_pytorch

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Deep Independently Recurrent Neural Network (IndRNN)

https://github.com/TaeSoo-Kim/TCNActionRecognition

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Interpretable 3D Human Action Analysis with Temporal Convolutional Networks

https://github.com/Tiiiger/SGC

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Simplifying Graph Convolutional Networks

https://github.com/Uason-Chen/CTR-GCN

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Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition

https://github.com/Va6lue/BST-Badminton-Stroke-type-Transformer

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BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports

https://github.com/Walter0807/MotionBERT

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MotionBERT: A Unified Perspective on Learning Human Motion Representations

https://github.com/YangDi666/UNIK

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UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition

https://github.com/YinxPeng/HA-CM-main

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Spatial Hierarchy and Temporal Attention Guided Cross Masking for Self-supervised Skeleton-based Action Recognition

https://github.com/ZhenyueQin/Angular-Skeleton-Encoding

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Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition

https://github.com/ZhouYuxuanYX/Hypergraph-Transformer-for-Skeleton-based-Action-Recognition

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Hypergraph Transformer for Skeleton-based Action Recognition

https://github.com/ZichenMiao/GNN-with-Low-rank-Learnable-Local-Filters

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Graph Convolution with Low-rank Learnable Local Filters

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