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Sep 2026
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Experiments on the IEMOCAP and MELD datasets show that Diff-IGAT significantly outperforms baseline methods, demonstrating its effectiveness in multi-party emotion recognition.
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Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition
Transformer-GAT is proposed, a hybrid framework that combines Transformer and the Graph Attention Network to enable cross-modal emotion understanding and effectively integrates multimodal features, balances global and local contexts, and provides deeper emotional insights, offering new directions for multimodal emotion...
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Open access
Nov 2026
HASF-Net: Hierarchical Attention and Spectral Filtering for Multimodal Emotion Recognition in Conversation
Multimodal emotion recognition in conversations (MERC) aims to identify utterance-level emotions from textual, acoustic, and visual cues, and provides a basis for future extension to richer multisensory affective understanding. However, semantic inconsistency across heterogeneous modalities, the under-utilization of mo...
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MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition
The multi-view text-guided multimodal fusion adapter (MVFA) is proposed, a parameter-efficient framework that augments frozen LLMs with strong multimodal reasoning capability and achieves state-of-the-art performance on key metrics while updating only a small fraction of parameters.
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Open access
Aug 2026
Learning Adaptive Cross-Modal Interactions for Multimodal Sentiment Analysis
A framework for learning adaptive cross-modal interactions for multimodal sentiment analysis that consistently outperforms previous methods and enhances multimodal representation capability for sentiment classification is proposed.
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· International Conference on... · 0 citations