@Article{cmc.2026.087279,
AUTHOR = {Yu Chen, Panpan Chen, Jun Wu, Shuai Guo, Jiahui Huang, Xinyi Zhu, Qun Zhang},
TITLE = {DMHG-LEDS: Joint Differentiated Modality-Aware Heterogeneous Graph and Local Emotion Difference Supervision for Multimodal Emotion Recognition in Conversations},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27984},
ISSN = {1546-2226},
ABSTRACT = {Multimodal Emotion Recognition in Conversations (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they apply shared or coarse-grained graph construction rules across modalities, overlooking their distinct dependency patterns; (2) they rely on fixed-activation MLPs for feature transformation, limiting nonlinear representation capacity in complex emotional scenarios; (3) they focus predominantly on contextual modeling while underexploring local emotion discrimination between related utterances. To address these issues, we propose Joint Differentiated Modality-Aware Heterogeneous Graph and Local Emotion Difference Supervision for Multimodal Emotion Recognition in Conversations (DMHG-LEDS), a novel MERC framework. Specifically, modality-aware heterogeneous graphs are constructed by assigning differentiated intra-modal connection strategies to text, visual, and audio modalities, enabling modality-dependent contextual relation modeling. Based on the resulting graph topology, graph convolution aggregates neighborhood information and Chebyshev-KAN subsequently performs adaptive nonlinear transformation within each propagation step. Finally, an Entropy-Gated Local Emotion Difference Supervision module is introduced as an auxiliary task. It constructs ordered utterance pairs within non-overlapping local segments and provides entropy-gated supervision based on emotion-label differences, thereby improving the discriminability of local utterance representations. Experiments on Interactive emotional dyadic motion capture database (IEMOCAP), A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation (MELD), and Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph (CMU-MOSEI) demonstrate the effectiveness of the proposed method, which outperforms all baselines.},
DOI = {10.32604/cmc.2026.087279}
}