Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition
Abstract In skeleton-based action recognition, abstracting the human body to skeletal representations often results in the loss of crucial information, which may result in misclassification of similar actions. To address this issue, we propose a Cross-scale Spatial Refinement Graph Convolutional Net...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-04-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00802-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849726431958925312 |
|---|---|
| author | Chengyuan Ke Sheng Liu Zhenghao Ke Yuan Feng Shengyong Chen |
| author_facet | Chengyuan Ke Sheng Liu Zhenghao Ke Yuan Feng Shengyong Chen |
| author_sort | Chengyuan Ke |
| collection | DOAJ |
| description | Abstract In skeleton-based action recognition, abstracting the human body to skeletal representations often results in the loss of crucial information, which may result in misclassification of similar actions. To address this issue, we propose a Cross-scale Spatial Refinement Graph Convolutional Network (CSR-GCN), which aims to improve action recognition accuracy by effectively capturing fine-grained features of skeleton sequences. In detail, we introduce an Attention-based Graph Pooling (AGP) module and a Cross-scale Feature Aggregation (CFA) module. The AGP module uses graph pooling to construct multi-scale skeletal sub-graphs, capturing implicit joint relationships and preserving crucial motion details. It retains global motion information while emphasizing local joint interactions, which enables a better understanding of dynamic changes in complex actions. Furthermore, the CFA module selectively integrates features from different spatial scales, enhancing feature distinctiveness while balancing global motion and local details. This multi-scale refinement of skeletal sequence representations, thereby capturing subtle dynamic changes in actions more precisely and enhancing the ability of the model to recognize and classify complex movement patterns. Finally, we validate the effectiveness of our method on three large-scale datasets, achieving superior accuracy compared to other state-of-the-art methods. |
| format | Article |
| id | doaj-art-eba52016d98745b785390319e87a4cf4 |
| institution | DOAJ |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-eba52016d98745b785390319e87a4cf42025-08-20T03:10:10ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-04-0118111710.1007/s44196-025-00802-xCross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action RecognitionChengyuan Ke0Sheng Liu1Zhenghao Ke2Yuan Feng3Shengyong Chen4Zhejiang University of TechnologyZhejiang University of TechnologyZhejiang University of TechnologyZhejiang University of TechnologyTianjin University of TechnologyAbstract In skeleton-based action recognition, abstracting the human body to skeletal representations often results in the loss of crucial information, which may result in misclassification of similar actions. To address this issue, we propose a Cross-scale Spatial Refinement Graph Convolutional Network (CSR-GCN), which aims to improve action recognition accuracy by effectively capturing fine-grained features of skeleton sequences. In detail, we introduce an Attention-based Graph Pooling (AGP) module and a Cross-scale Feature Aggregation (CFA) module. The AGP module uses graph pooling to construct multi-scale skeletal sub-graphs, capturing implicit joint relationships and preserving crucial motion details. It retains global motion information while emphasizing local joint interactions, which enables a better understanding of dynamic changes in complex actions. Furthermore, the CFA module selectively integrates features from different spatial scales, enhancing feature distinctiveness while balancing global motion and local details. This multi-scale refinement of skeletal sequence representations, thereby capturing subtle dynamic changes in actions more precisely and enhancing the ability of the model to recognize and classify complex movement patterns. Finally, we validate the effectiveness of our method on three large-scale datasets, achieving superior accuracy compared to other state-of-the-art methods.https://doi.org/10.1007/s44196-025-00802-xGraph convolutional networkAction recognitionSkeleton-basedGraph pooling |
| spellingShingle | Chengyuan Ke Sheng Liu Zhenghao Ke Yuan Feng Shengyong Chen Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition International Journal of Computational Intelligence Systems Graph convolutional network Action recognition Skeleton-based Graph pooling |
| title | Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition |
| title_full | Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition |
| title_fullStr | Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition |
| title_full_unstemmed | Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition |
| title_short | Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition |
| title_sort | cross scale spatial refinement graph convolutional network for skeleton based action recognition |
| topic | Graph convolutional network Action recognition Skeleton-based Graph pooling |
| url | https://doi.org/10.1007/s44196-025-00802-x |
| work_keys_str_mv | AT chengyuanke crossscalespatialrefinementgraphconvolutionalnetworkforskeletonbasedactionrecognition AT shengliu crossscalespatialrefinementgraphconvolutionalnetworkforskeletonbasedactionrecognition AT zhenghaoke crossscalespatialrefinementgraphconvolutionalnetworkforskeletonbasedactionrecognition AT yuanfeng crossscalespatialrefinementgraphconvolutionalnetworkforskeletonbasedactionrecognition AT shengyongchen crossscalespatialrefinementgraphconvolutionalnetworkforskeletonbasedactionrecognition |