7 citations · 37 across the 30 of their papers we have counts for
12 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…
SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment
Wei Ai, Yinghui Gao, Jianbin Li +4
Entity alignment is crucial for merging knowledge across knowledge graphs, as it matches entities with identical semantics. The standard method matches these entities based on thei…
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
Tao Meng, Fuchen Zhang, Yuntao Shou +3
Efficiently capturing consistent and complementary semantic features in a multimodal conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC). Exis…
Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion
Yuntao Shou, Tao Meng, Fuchen Zhang +2
Multi-modal Emotion Recognition in Conversation (MERC) has received considerable attention in various fields, e.g., human-computer interaction and recommendation systems. Most exis…
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning
Wei Ai, FuChen Zhang, Tao Meng +3
In terms of human-computer interaction, it is becoming more and more important to correctly understand the user's emotional state in a conversation, so the task of multimodal emoti…
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition
Yuntao Shou, Tao Meng, Wei Ai +2
With the release of increasing open-source emotion recognition datasets on social media platforms and the rapid development of computing resources, multimodal emotion recognition t…
DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition
Wei Ai, Yuntao Shou, Tao Meng +2
With the continuous development of deep learning (DL), the task of multimodal dialogue emotion recognition (MDER) has recently received extensive research attention, which is also…