arXiv AI By Linan Zhu, Zihao Zhai, Mengfei Xu, Xiao Han, Yuqian Fu, Xiangfan Chen, Zihang Zhang

CLEAR: Coupling Local Evidence with Affective Representations for Multimodal Emotion Recognition in Conversation

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arXiv Computer Vision
Oct 5

Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild

The paper compares text‑based and feature‑based models for recognizing compound emotions in real‑world videos. It proposes textualizing non‑verbal cues from audio and visual modalities into text to leverage large language models, while feature‑based models directly combine extracted multimodal features. Experiments on the C‑EXPR‑DB dataset show that feature‑based models outperform textualization in the wild, though textual models can excel when rich transcripts are available.

By Nicolas Richet, Soufiane Belharbi, Haseeb Aslam, Meike Emilie Schadt, Manuela Gonz\'alez-Gonz\'alez, Gustave Cortal, Alessandro Lameiras Koerich, Marco Pedersoli, Alain Finkel, Simon Bacon, Eric Granger
arXiv AI
Sep 24

BiCFlow-MER: Orchestrating Discriminative and Generative Multimodal Emotion Recognition via Conditional Transport

BiCFlow-MER introduces a conditional-flow framework for audio-text multimodal emotion recognition, treating the task as generative evidence transport within a structured emotion space. It disentangles emotion-oriented evidence from speaker style and lexical content, creating a conflict-aware affective condition that guides bidirectional rectified flow to an explicit emotion-space endpoint. The model verifies candidate emotions via adaptive prototype-cloud scoring and backward class-to-condition consistency, achieving superior performance on IEMOCAP, MELD, and the zero-shot CASE benchmark.

By Yanbing Wang, Shenyue Wang, Chunyang Yu