Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition

• URL: http://arxiv.org/abs/2209.10073v1
• Date: Wed, 21 Sep 2022 02:33:07 GMT
• Title: Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
• Authors: Anqi Zhu, Qiuhong Ke, Mingming Gong and James Bailey
• Abstract summary: We present an Adaptive Local-Component-aware Graph Convolutional Network for skeleton-based action recognition. Our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.
• Score: 54.23513799338309
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant to actions. To further improve the sample efficiency, meta-learning-based one-shot learning solutions were developed for skeleton-based action recognition. These methods find the nearest neighbor according to the similarity between instance-level global average embedding. However, such measurement holds unstable representativity due to inadequate generalized learning on local invariant and noisy features, while intuitively, more fine-grained recognition usually relies on determining key local body movements. To address this limitation, we present the Adaptive Local-Component-aware Graph Convolutional Network, which replaces the comparison metric with a focused sum of similarity measurements on aligned local embedding of action-critical spatial/temporal segments. Comprehensive one-shot experiments on the public benchmark of NTU-RGB+D 120 indicate that our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.

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