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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

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arxiv 1801.07455 v2 pith:HCUXUSFX submitted 2018-01-23 cs.CV

Sijie Yan , Yuanjun Xiong , Dahua Lin This is my paper
classification cs.CV
keywords skeletonsactionconvolutionalexpressivegeneralizationgraphhumanmethods
verification ladder T0 review T1 audit T2 compute T3 formal
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Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Physical Self-Supervised Learning: IMU Sensing without Manual Labels

    cs.LG 2026-07 conditional novelty 7.0 of 10

    A physics-based self-supervised autoencoder achieves label-free IMU tracking and motion capture that outperforms supervised baselines in generalization tests.

  2. Enhancing Sports Strategy with Video Analytics and Data Mining: Automated Video-Based Analytics Framework for Tennis Doubles

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A tennis doubles annotation framework is built and evaluated, showing transfer-learned CNNs outperform pose-only GCNs for automated shot and formation labeling.

  3. CascadeFormer: A Family of Two-stage Cascading Transformers for Skeleton-based Human Action Recognition

    cs.CV 2025-08 conditional novelty 4.0 of 10

    A masked-pretrained skeleton transformer with a second fine-tuning transformer and cross-attention fusion reaches 94.66% on Penn Action, 91.16% on N-UCLA, and 81.01%/88.17% on NTU RGB+D 60 cross-subject/cross-view.

  4. Variational Graph Convolutional Neural Networks

    cs.LG 2025-07 conditional novelty 4.0 of 10

    Variational graph convolutional networks that sample layer outputs from learned Gaussians provide uncertainty estimates and small accuracy improvements on social trading and skeleton action recognition benchmarks.

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