7 citations · 37 across the 34 of their papers we have counts for
6 papers · 1 filter
A Flow Model with Low-Rank Transformers for Incomplete Multimodal Survival Analysis
Yi Yin, Yuntao Shou, Zao Dai +4
In recent years, multimodal medical data-based survival analysis has attracted much attention. However, real-world datasets often suffer from the problem of incomplete modality, wh…
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…
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…
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…
LRA-GNN: Latent Relation-Aware Graph Neural Network with Initial and Dynamic Residual for Facial Age Estimation
Yiping Zhang, Yuntao Shou, Wei Ai +2
Face information is mainly concentrated among facial key points, and frontier research has begun to use graph neural networks to segment faces into patches as nodes to model comple…
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…