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Automated social behaviour analysis of mice has become an increasinglypopular research area in behavioural neuroscience. Recently, pose information(i.e., locations of keypoints or skeleton) has been used to interpret socialbehaviours of mice. Nevertheless, effective encoding and decoding of socialinteraction information underlying the keypoints of mice has been rarelyinvestigated in the existing methods. In particular, it is challenging to modelcomplex social interactions between mice due to highly deformable body shapesand ambiguous movement patterns. To deal with the interaction modellingproblem, we here propose a Cross-Skeleton Interaction Graph Aggregation Network(CS-IGANet) to learn abundant dynamics of freely interacting mice, where aCross-Skeleton Node-level Interaction module (CS-NLI) is used to modelmulti-level interactions (i.e., intra-, inter- and cross-skeletoninteractions). Furthermore, we design a novel Interaction-Aware Transformer(IAT) to dynamically learn the graph-level representation of social behavioursand update the node-level representation, guided by our proposedinteraction-aware self-attention mechanism. Finally, to enhance therepresentation ability of our model, an auxiliary self-supervised learning taskis proposed for measuring the similarity between cross-skeleton nodes.Experimental results on the standard CRMI13-Skeleton and our PDMB-Skeletondatasets show that our proposed model outperforms several otherstate-of-the-art approaches.

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