STD-Explain: Generalizing explanations for spatio-temporal graph convolutional networks based on spatio-temporal decoupled perturbation

Spatio-temporal graph convolutional networks utilize an alternating combination of one-dimensional ordinary convolution and graph convolution to extract spatio-temporal features. This alternation intertwines temporal and spatial features closely, leading to a tight coupling between them. The presenc...

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Bibliographic Details
Published in:	Neurocomputing (Amsterdam) Vol. 657; p. 131539
Main Authors:	Li, Yanshan, Shi, Ting, He, Suixuan, Chen, Zhiyuan, Zhang, Li, Yu, Rui, Xie, Weixin
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 07.12.2025
Subjects:	Explainability algorithm Human skeleton action recognition Spatio temporal graph convolutional networks Spatio-temporal decoupling Human skeleton action recognition Spatio-temporal decoupling Explainability algorithm Spatio temporal graph convolutional networks
ISSN:	0925-2312
Online Access:	Get full text
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Summary:	Spatio-temporal graph convolutional networks utilize an alternating combination of one-dimensional ordinary convolution and graph convolution to extract spatio-temporal features. This alternation intertwines temporal and spatial features closely, leading to a tight coupling between them. The presence of spatio-temporal coupling complicates the analysis of spatio-temporal data, posing challenges for existing explainability algorithms to effectively separate and interpret these intertwined features. Therefore, we propose STD-Explain, an explainable algorithm based on spatio-temporal decoupled perturbation, which employs a two-stage perturbation approach considering subgraph and node-level explanations. Firstly, targeting the spatio-temporal coupling issue in spatio-temporal graph convolutional networks, the algorithm proposes a temporal perturbation algorithm based on Slice Graph and a spatial perturbation algorithm aimed at important subgraph node features. Secondly, to avoid introducing additional semantic information when extracting temporal subgraphs, we propose a method for generating temporal subgraphs in spatio-temporal decoupling, slicing human skeleton sequences with discrete masks to ensure each subsequence maintains spatial structure integrity without introducing additional edges. Furthermore, to ensure the maximum correlation between the interpreted subgraphs and model predictions, we propose a temporal important subgraph discrimination strategy to select the most relevant subgraphs to model predictions. Experimental results demonstrate that STD-Explain performs well in qualitative and quantitative analysis.
ISSN:	0925-2312
DOI:	10.1016/j.neucom.2025.131539