CH-SIMS

SeRIn: Decomposed multimodal fusion architecture achieves state-of-the-art on sentiment analysis benchmarks

Researchers propose SeRIn (Segregate, Refine, Integrate), a multimodal language model fusion scheme that separates modality-specific refinement from cross-modal interaction via distinct architectural pathways. The design defers full cross-modal interaction to a final prediction step, with ablations showing structured interaction rather than added capacity drives performance gains. SeRIn achieves state-of-the-art results on CH-SIMS and CMU-MOSEI benchmarks, and exhibits emergent modality reweighting under visual corruption without explicit supervision.