Grading-inspired complementary enhancing for multimodal sentiment analysis

Abstract

Due to its strong capacity for integrating heterogeneous multi-source information, multimodal sentiment analysis (MSA) has achieved remarkable progress in affective computing. However, existing methods typically adopt symmetric fusion strategies that treat all modalities equally, overlooking their inherent performance disparities that some modalities excel at discriminative representation, while others carry underutilized supportive cues. This limitation leads to insufficiency in cross-modal complementary correlation exploration. To address this issue, we propose a novel Grading-Inspired Complementary Enhancing (GCE) framework for MSA, which is one of the first attempts to conduct dynamic assessment for knowledge transfer in progressive multimodal fusion and cooperation. Specifically, based on cross-modal interaction, a task-aware grading mechanism categorizes modality-pair associations into dominant (high-performing) and supplementary (low-performing) branches according to their task performance. Accordingly, a relation filtering module selectively identifies the trustworthy information from the dominant branch to enhance consistency exploration in supplementary modality pairs with minimized redundancy. Afterwards, a weight adaptation module is adopted to dynamically adjust the guiding weight of individual samples for adaptability and generalization. Extensive experiments conducted on three benchmark datasets evidence that our proposed GCE approach can outperform the state-of-the-art MSA methods. Our code is available at https://github.com/hka-7/GCEforMSA.