Segregate, Refine, Integrate: Decomposing Multimodal Fusion for Sentiment Analysis

2026-07-15 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

SeRIn (Segregate, Refine, Integrate) is a novel multimodal Large Language Model fusion scheme designed for sentiment analysis, which architecturally separates unimodal signal refinement from cross-modal interaction. This framework partitions learnable fusion tokens into modality-specific (audio, visual) and cross-modal (audiovisual) groups, enforcing isolated pathways through modality-constrained attention masks. Representations are refined using Internally Gated Cross-Attention and Internally Gated Self-Attention, with unrestricted cross-modal interaction deferred to a final Integration Head. SeRIn achieves state-of-the-art performance on both the CH-SIMS and CMU-MOSEI benchmarks, improving all reported metrics. Crucially, ablation studies demonstrate that these gains stem from the imposed interaction topology, rather than increased model capacity. For instance, on SIMS, SeRIn improves Acc2 by +1.72 and F1 by +1.65, while on MOSEI, it boosts Acc2 by +1.02 and F1 by +1.01.

Key takeaway

For Machine Learning Engineers designing multimodal fusion architectures, particularly for sentiment analysis, you should prioritize explicit architectural segregation of unimodal refinement and cross-modal integration. Implementing modality-constrained attention masks and deferring full cross-modal interaction to a final prediction stage, as demonstrated by SeRIn, can yield state-of-the-art performance. This approach offers superior results by structuring information flow, rather than relying solely on increased model capacity or optimization penalties.

Key insights

Architecturally separating unimodal refinement from cross-modal integration significantly enhances multimodal fusion performance.

Principles

• Interaction topology is a distinct and crucial design axis for multimodal fusion.
• Unimodal refinement and cross-modal integration should be distinct processing stages.
• Asymmetric read-only cross-modal pathways prevent contamination while aggregating states.

Method

Segregate fusion tokens into modality-specific and cross-modal groups via parameter-free attention masks. Refine pathways using Internally Gated Cross-Attention and Internally Gated Self-Attention. Integrate representations only at the final prediction step.

In practice

• Apply modality-constrained attention masks to enforce structural separation in multimodal LMs.
• Use content-dependent element-wise gating within attention for fine-grained flow control.
• Defer unrestricted cross-modal interaction to a dedicated final prediction layer.

Topics

• Multimodal Sentiment Analysis
• Multimodal Fusion
• Attention Mechanisms
• Language Models
• Interaction Topology
• Gated Attention

Code references

• alexisfilippakopoulos/SeRIn-MSA

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.