Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour

• URL: http://arxiv.org/abs/2208.03819v1
• Date: Sun, 7 Aug 2022 21:06:42 GMT
• Title: Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour
• Authors: Feixiang Zhou, Xinyu Yang, Fang Chen, Long Chen, Zheheng Jiang, Hui Zhu, Reiko Heckel, Haikuan Wang, Minrui Fei and Huiyu Zhou
• Abstract summary: Social behaviour analysis of mice has become an increasingly popular research area in behavioural neuroscience. It is challenging to model complex social interactions between mice due to highly deformable body shapes and ambiguous movement patterns. We propose a Cross-Skeleton Interaction Graph Aggregation Network (CS-IGANet) to learn abundant dynamics of freely interacting mice.
• Score: 24.716092330419123
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Automated social behaviour analysis of mice has become an increasingly popular research area in behavioural neuroscience. Recently, pose information (i.e., locations of keypoints or skeleton) has been used to interpret social behaviours of mice. Nevertheless, effective encoding and decoding of social interaction information underlying the keypoints of mice has been rarely investigated in the existing methods. In particular, it is challenging to model complex social interactions between mice due to highly deformable body shapes and ambiguous movement patterns. To deal with the interaction modelling problem, we here propose a Cross-Skeleton Interaction Graph Aggregation Network (CS-IGANet) to learn abundant dynamics of freely interacting mice, where a Cross-Skeleton Node-level Interaction module (CS-NLI) is used to model multi-level interactions (i.e., intra-, inter- and cross-skeleton interactions). Furthermore, we design a novel Interaction-Aware Transformer (IAT) to dynamically learn the graph-level representation of social behaviours and update the node-level representation, guided by our proposed interaction-aware self-attention mechanism. Finally, to enhance the representation ability of our model, an auxiliary self-supervised learning task is proposed for measuring the similarity between cross-skeleton nodes. Experimental results on the standard CRMI13-Skeleton and our PDMB-Skeleton datasets show that our proposed model outperforms several other state-of-the-art approaches.

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