Adaptive Local-Component-Aware Graph Convolutional Network for One-Shot Skeleton-Based Action Recognition

Anqi Zhu, Qiuhong Ke, Mingming Gong, James Bailey; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 6038-6047

Abstract


Skeleton-based action recognition receives increasing attention because skeleton sequences reduce training complexity by eliminating visual information irrelevant to actions. To further improve sample efficiency, meta-learning-based one-shot learning solutions were developed for skeleton-based action recognition. These methods predict by finding the nearest neighbors according to the similarity between instance-level global embedding. However, such measurement holds unstable representativity due to inadequate generalized learning on the averaged local invariant and noisy features, while intuitively, steady and fine-grained recognition 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.

Related Material


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[bibtex]
@InProceedings{Zhu_2023_WACV, author = {Zhu, Anqi and Ke, Qiuhong and Gong, Mingming and Bailey, James}, title = {Adaptive Local-Component-Aware Graph Convolutional Network for One-Shot Skeleton-Based Action Recognition}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6038-6047} }