MIDG: Mixture of Invariant Experts with knowledge injection for Domain Generalization in Multimodal Sentiment Analysis

• URL: http://arxiv.org/abs/2512.07430v1
• Date: Mon, 08 Dec 2025 11:04:00 GMT
• Title: MIDG: Mixture of Invariant Experts with knowledge injection for Domain Generalization in Multimodal Sentiment Analysis
• Authors: Yangle Li, Danli Luo, Haifeng Hu,
• Abstract summary: We propose a novel framework for domain generalization for Multimodal Sentiment Analysis (MSA)<n>The framework incorporates a Mixture of Invariant Experts model to extract domain-invariant features.<n> Secondly, we design a Cross-Modal Adapter to augment the semantic richness of multimodal representations through cross-modal knowledge injection.
• Score: 11.688062963416117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing methods in domain generalization for Multimodal Sentiment Analysis (MSA) often overlook inter-modal synergies during invariant features extraction, which prevents the accurate capture of the rich semantic information within multimodal data. Additionally, while knowledge injection techniques have been explored in MSA, they often suffer from fragmented cross-modal knowledge, overlooking specific representations that exist beyond the confines of unimodal. To address these limitations, we propose a novel MSA framework designed for domain generalization. Firstly, the framework incorporates a Mixture of Invariant Experts model to extract domain-invariant features, thereby enhancing the model's capacity to learn synergistic relationships between modalities. Secondly, we design a Cross-Modal Adapter to augment the semantic richness of multimodal representations through cross-modal knowledge injection. Extensive domain experiments conducted on three datasets demonstrate that the proposed MIDG achieves superior performance.

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