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- W4306177316 abstract "Graph convolution networks (GCN) have been widely used in skeleton-based action recognition. We note that existing GCN-based approaches primarily rely on prescribed graphical structures (ie., a manually defined topology of skeleton joints), which limits their flexibility to capture complicated correlations between joints. To move beyond this limitation, we propose a new framework for skeleton-based action recognition, namely Dynamic Group Spatio-Temporal GCN (DG-STGCN). It consists of two modules, DG-GCN and DG-TCN, respectively, for spatial and temporal modeling. In particular, DG-GCN uses learned affinity matrices to capture dynamic graphical structures instead of relying on a prescribed one, while DG-TCN performs group-wise temporal convolutions with varying receptive fields and incorporates a dynamic joint-skeleton fusion module for adaptive multi-level temporal modeling. On a wide range of benchmarks, including NTURGB+D, Kinetics-Skeleton, BABEL, and Toyota SmartHome, DG-STGCN consistently outperforms state-of-the-art methods, often by a notable margin." @default.
- W4306177316 created "2022-10-14" @default.
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- W4306177316 date "2022-10-11" @default.
- W4306177316 modified "2023-09-30" @default.
- W4306177316 title "DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition" @default.
- W4306177316 doi "https://doi.org/10.48550/arxiv.2210.05895" @default.
- W4306177316 hasPublicationYear "2022" @default.
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