HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
arxiv(2024)
摘要
Skeleton-based action recognition has gained considerable traction thanks to
its utilization of succinct and robust skeletal representations. Nonetheless,
current methodologies often lean towards utilizing a solitary backbone to model
skeleton modality, which can be limited by inherent flaws in the network
backbone. To address this and fully leverage the complementary characteristics
of various network architectures, we propose a novel Hybrid Dual-Branch Network
(HDBN) for robust skeleton-based action recognition, which benefits from the
graph convolutional network's proficiency in handling graph-structured data and
the powerful modeling capabilities of Transformers for global information. In
detail, our proposed HDBN is divided into two trunk branches: MixGCN and
MixFormer. The two branches utilize GCNs and Transformers to model both 2D and
3D skeletal modalities respectively. Our proposed HDBN emerged as one of the
top solutions in the Multi-Modal Video Reasoning and Analyzing Competition
(MMVRAC) of 2024 ICME Grand Challenge, achieving accuracies of 47.95
75.36
existing methods. Our code will be publicly available at:
https://github.com/liujf69/ICMEW2024-Track10.
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