19 papers
CMOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning
Yuntao Shou, Tao Meng, Wei Ai +1
Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs. However, real-world data often suffer from missing m…
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…
Dual-branch Graph Domain Adaptation for Cross-scenario Multi-modal Emotion Recognition
Yuntao Shou, Jun Zhou, Tao Meng +2
Multimodal Emotion Recognition in Conversations (MERC) aims to predict speakers' emotional states in multi-turn dialogues through text, audio, and visual cues. In real-world settin…
Relational graph-driven differential denoising and diffusion attention fusion for multimodal conversation emotion recognition
Ying Liu, Yuntao Shou, Wei Ai +2
In real-world scenarios, audio and video signals are often subject to environmental noise and limited acquisition conditions, resulting in extracted features containing excessive n…
Dynamic Fusion-Aware Graph Convolutional Neural Network for Multimodal Emotion Recognition in Conversations
Tao Meng, Weilun Tang, Yuntao Shou +4
Multimodal emotion recognition in conversations (MERC) aims to identify and understand the emotions expressed by speakers during utterance interaction from multiple modalities (e.g…
AMB-DSGDN: Adaptive Modality-Balanced Dynamic Semantic Graph Differential Network for Multimodal Emotion Recognition
Yunsheng Wang, Yuntao Shou, Yilong Tan +3
Multimodal dialogue emotion recognition captures emotional cues by fusing text, visual, and audio modalities. However, existing approaches still suffer from notable limitations in…