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Article overview
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Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition | | Tailin Chen
; Desen Zhou
; Jian Wang
; Shidong Wang
; Yu Guan
; Xuming He
; Errui Ding
; | | Date: |
10 Aug 2021 | | Abstract: | The task of skeleton-based action recognition remains a core challenge in
human-centred scene understanding due to the multiple granularities and large
variation in human motion. Existing approaches typically employ a single neural
representation for different motion patterns, which has difficulty in capturing
fine-grained action classes given limited training data. To address the
aforementioned problems, we propose a novel multi-granular spatio-temporal
graph network for skeleton-based action classification that jointly models the
coarse- and fine-grained skeleton motion patterns. To this end, we develop a
dual-head graph network consisting of two interleaved branches, which enables
us to extract features at two spatio-temporal resolutions in an effective and
efficient manner. Moreover, our network utilises a cross-head communication
strategy to mutually enhance the representations of both heads. We conducted
extensive experiments on three large-scale datasets, namely NTU RGB+D 60, NTU
RGB+D 120, and Kinetics-Skeleton, and achieves the state-of-the-art performance
on all the benchmarks, which validates the effectiveness of our method. | | Source: | arXiv, 2108.04536 | | Services: | Forum | Review | PDF | Favorites | |
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