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arXiv · 2508.03722

Multimodal Video Emotion Recognition with Reliable Reasoning Priors

Zhepeng Wang·Yingjian Zhu·Guanghao Dong·Hongzhu Yi·Feng Chen·Xinming Wang·Jun Xie
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Abstract

This study investigates the integration of trustworthy prior reasoning knowledge from MLLMs into multimodal emotion recognition. We employ Gemini to generate fine-grained, modality-separable reasoning traces, which are injected as priors during the fusion stage to enrich cross-modal interactions. To mitigate the pronounced class-imbalance in multimodal emotion recognition, we introduce Balanced Dual-Contrastive Learning, a loss formulation that jointly balances inter-class and intra-class distributions. Applied to the MER2024 benchmark, our prior-enhanced framework yields substantial performance gains, demonstrating that the reliability of MLLM-derived reasoning can be synergistically combined with the domain adaptability of lightweight fusion networks for robust, scalable emotion recognition.

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Zhepeng Wang, Yingjian Zhu, Guanghao Dong, Hongzhu Yi, Feng Chen, Xinming Wang, Jun Xie. 2025-07-29. Multimodal Video Emotion Recognition with Reliable Reasoning Priors. https://arxiv.org/abs/2508.03722

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