11 papers · 1 filter
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…
Multimodal Large Language Models Meet Multimodal Emotion Recognition and Reasoning: A Survey
Yuntao Shou, Tao Meng, Wei Ai +1
In recent years, large language models (LLMs) have driven major advances in language understanding, marking a significant step toward artificial general intelligence (AGI). With in…
SE-GNN: Seed Expanded-Aware Graph Neural Network with Iterative Optimization for Semi-supervised Entity Alignment
Tao Meng, Shuo Shan, Hongen Shao +3
Entity alignment aims to use pre-aligned seed pairs to find other equivalent entities from different knowledge graphs (KGs) and is widely used in graph fusion-related fields. Howev…
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation
Yuntao Shou, Tao Meng, Wei Ai +1
Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying human emotional states by combining data from multiple different modalities (e.g., audio…
SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation
Tao Meng, Wei Ai, Jianbin Li +3
Text representation learning is significant as the cornerstone of natural language processing. In recent years, graph contrastive learning (GCL) has been widely used in text repres…
SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition
Fangze Fu, Wei Ai, Fan Yang +3
Multimodal Emotion Recognition in Conversations (MERC) aims to classify utterance emotions using textual, auditory, and visual modal features. Most existing MERC methods assume eac…