D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection

Multimodal sentiment detection aims to classify the sentiment polarity of a given imagetext pair. Existing approaches apply the same fixed framework to all input samples, lacking the flexibility to adapt to different image-text pairs. Furthermore, the interaction patterns of these methods are overly homogenized, limiting the model's capacity to extract multimodal sentiment information effectively. In this paper, we develop a Dual-Branch Dynamic Routing Network (D 2 R), which is the first multimodal dynamic interaction model towards multimodal sentiment detection. Specifically, we design six independent units to simulate inter-and intramodal information interactions without depending on any existing fixed frameworks. Additionally, we configure a soft router in each unit to guide path generation and introduce the path regularization term to optimize these inference paths. Comprehensive experiments on three publicly available datasets demonstrate the superiority of our proposed model over state-ofthe-art methods.