<ul><li>Recognizing interactive actions, including hand-to-hand interaction and human-to-human interaction, has attracted increasing attention.</li><li>A mutual excitation graph convolutional network (me-GCN) is proposed to model the semantic relationships between entities.</li><li>me-GC utilizes a mutual topology excitation module to model the mutual constraints between individual entities.</li><li>Experimental results demonstrate the superiority of me-GCN over existing GCN-based and Transformer-based methods.</li></ul>

Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition

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