Adaptive Key Role Guided Hierarchical Relation Inference for Enhanced Group-level Emotion Recognition

In this paper, we propose a novel hierarchical relational network, termed Key Role Guided Hierarchical Relation Inference (KR-HRI), for enhanced group-level emotion recognition (GER). Unlike existing methods that adopt a coarse-grained approach to model interactions among all individuals, our approach adaptively identifies and emphasizes key individuals who play a crucial role in conveying group-level emotions. By integrating coarse-grained relationship modeling with fine-grained key individual enhancement and leveraging global scene information, our method effectively refines discriminative feature generation while minimizing irrelevant interference. We introduce a Multi-branch Interaction Module (MIM) to dynamically fuse features from both the global scene and local individual branches using a localized mask integration strategy. This comprehensive approach enhances the interaction between global and local features, resulting in robust group-level emotion representations. Extensive experiments on three widely adopted GER datasets demonstrate that our framework consistently outperforms state-of-the-art methods, validating the effectiveness and robustness of our proposed approach.