7 citations · 37 across the 34 of their papers we have counts for
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cs.SD2026
Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition
Chengling Guo, Yuntao Shou, Tao Meng +3
Multimodal emotion recognition in conversations aims to infer utterance-level emotions by jointly modeling textual, acoustic, and visual cues within context. Despite recent progres…
cs.SD2025
GSDNet: Revisiting Incomplete Multimodal-Diffusion from Graph Spectrum Perspective for Conversation Emotion Recognition
Yuntao Shou, Jun Yao, Tao Meng +3
Multimodal emotion recognition in conversations (MERC) aims to infer the speaker's emotional state by analyzing utterance information from multiple sources (i.e., video, audio, and…
cs.SD2023★ 1 cited
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations
Tao Meng, Yuntao Shou, Wei Ai +2
The main task of Multimodal Emotion Recognition in Conversations (MERC) is to identify the emotions in modalities, e.g., text, audio, image and video, which is a significant develo…