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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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 657; S. 131539 |
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| Sprache: | Englisch |
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Elsevier B.V
07.12.2025
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| ISSN: | 0925-2312 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 131539 |
| Author | He, Suixuan Chen, Zhiyuan Xie, Weixin Li, Yanshan Zhang, Li Yu, Rui Shi, Ting |
| Author_xml | – sequence: 1 givenname: Yanshan orcidid: 0000-0002-8814-4628 surname: Li fullname: Li, Yanshan email: lys@szu.edu.cn organization: Institute of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China – sequence: 2 givenname: Ting surname: Shi fullname: Shi, Ting organization: Institute of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China – sequence: 3 givenname: Suixuan surname: He fullname: He, Suixuan organization: Institute of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China – sequence: 4 givenname: Zhiyuan surname: Chen fullname: Chen, Zhiyuan organization: Institute of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China – sequence: 5 givenname: Li surname: Zhang fullname: Zhang, Li organization: Institute of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China – sequence: 6 givenname: Rui surname: Yu fullname: Yu, Rui organization: Institute of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China – sequence: 7 givenname: Weixin surname: Xie fullname: Xie, Weixin organization: Institute of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China |
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| Cites_doi | 10.1109/TNNLS.2021.3061115 10.1016/j.inffus.2025.103387 10.1109/TMM.2021.3121559 10.1109/TMM.2021.3086758 10.1007/s11263-025-02393-8 10.1109/TPAMI.2021.3115452 10.1109/TPAMI.2019.2916873 10.1109/TKDE.2022.3187455 10.1109/TMM.2022.3233442 10.1016/j.patcog.2020.107293 10.1109/TAFFC.2022.3181053 10.1109/TPAMI.2024.3413026 10.1109/TPAMI.2024.3367416 10.1016/j.patcog.2024.110251 10.1109/TNNLS.2022.3152990 10.1007/s10462-020-09904-8 10.1109/TKDE.2022.3201170 10.1016/j.neucom.2024.128393 |
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| Keywords | Human skeleton action recognition Spatio-temporal decoupling Explainability algorithm Spatio temporal graph convolutional networks |
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