DWnet: Deep-Wide Network for 3D Action Recognition
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Abstract (arXiv)
We propose in this paper a deep-wide network (DWnet) which combines the deep structure with the broad learning system (BLS) to recognize actions. Compared with the deep structure, the novel model saves lots of testing time and almost achieves real-time testing. Furthermore, the DWnet can capture better features than broad learning system can. In terms of methodology, we use pruned hierarchical co-occurrence network (PruHCN) to learn local and global spatial-temporal features. To obtain sufficient global information, BLS is used to expand features extracted by PruHCN. Experiments on two common skeletal datasets demonstrate the advantage of the proposed model on testing time and the effectiveness of the novel model to recognize the action.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Aug 2019.
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Citing papers per year: 2023: 3.
3 papers.
- 2023Lifting by Image -- Leveraging Image Cues for Accurate 3D Human Pose Estimationcited by 6
- 2023A Generically Contrastive Spatiotemporal Representation Enhancement for 3D Skeleton Action Recognitioncited by 1
- 2023SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognitioncited by 0
- 2023Lifting by Image -- Leveraging Image Cues for Accurate 3D Human Pose Estimation25 Dec 2023
- 2023A Generically Contrastive Spatiotemporal Representation Enhancement for 3D Skeleton Action Recognition23 Dec 2023
- 2023SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition30 Aug 2023