Feature Separation Graph Convolutional Networks for Skeleton-Based Action Recognition

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dc.contributor.authorZhang, Lingyanen_US
dc.contributor.authorLing, Wanyuen_US
dc.contributor.authorDaizhou, Shuwenen_US
dc.contributor.authorKuang, Lien_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:05:32Z
dc.date.available2024-10-13T18:05:32Z
dc.date.issued2024
dc.description.abstractGraph Convolutional Networks have made significant advancements in skeleton-based action recognition. However, most existing methods process body features globally, overlooking the challenges posed by partial visual occlusion, which severely impair the model's recognition capabilities when body parts are obscured. To address this issue, we propose Feature Separation Graph Convolutional Networks (FS-GCN), consisting of Feature Separation Modeling (FSM) and Exchange Modeling (EM). FSM strategically separates the skeleton feature into essential body parts, placing emphasis on upper body features while seamlessly integrating lower body features. This allows FS-GCN to better capture the distinctive spatial and temporal characteristics associated with each body segment. EM facilitates the swapping of body half correlation matrices between different graph convolution modules, eliminating discrepancies and enabling a more robust and unified global information processing framework. Furthermore, FS-GCN divides the adaptive graph into two key parts for graph contrastive learning to extract more intra-class contrastive information during training process. FS-GCN achieves state-of-the-art performance on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets, especially in line-of-sight-obstructed scenarios.en_US
dc.description.sectionheadersHuman I
dc.description.seriesinformationPacific Graphics Conference Papers and Posters
dc.identifier.doi10.2312/pg.20241317
dc.identifier.isbn978-3-03868-250-9
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20241317
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20241317
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Activity recognition and understanding; Neural networks
dc.subjectComputing methodologies → Activity recognition and understanding
dc.subjectNeural networks
dc.titleFeature Separation Graph Convolutional Networks for Skeleton-Based Action Recognitionen_US
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