One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching

• URL: http://arxiv.org/abs/2307.07286v2
• Date: Tue, 6 Feb 2024 12:02:40 GMT
• Title: One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching
• Authors: Siyuan Yang, Jun Liu, Shijian Lu, Er Meng Hwa, Alex C. Kot
• Abstract summary: One-shot skeleton action recognition aims to learn a skeleton action recognition model with a single training sample. This paper presents a novel one-shot skeleton action recognition technique that handles skeleton action recognition via multi-scale spatial-temporal feature matching.
• Score: 77.6989219290789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One-shot skeleton action recognition, which aims to learn a skeleton action recognition model with a single training sample, has attracted increasing interest due to the challenge of collecting and annotating large-scale skeleton action data. However, most existing studies match skeleton sequences by comparing their feature vectors directly which neglects spatial structures and temporal orders of skeleton data. This paper presents a novel one-shot skeleton action recognition technique that handles skeleton action recognition via multi-scale spatial-temporal feature matching. We represent skeleton data at multiple spatial and temporal scales and achieve optimal feature matching from two perspectives. The first is multi-scale matching which captures the scale-wise semantic relevance of skeleton data at multiple spatial and temporal scales simultaneously. The second is cross-scale matching which handles different motion magnitudes and speeds by capturing sample-wise relevance across multiple scales. Extensive experiments over three large-scale datasets (NTU RGB+D, NTU RGB+D 120, and PKU-MMD) show that our method achieves superior one-shot skeleton action recognition, and it outperforms the state-of-the-art consistently by large margins.

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