7 citations · 37 across the 34 of their papers we have counts for
34 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…
The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection
Wei Ai, Yilong Tan, Yuntao Shou +4
In recent years, the rapid evolution of large vision-language models (LVLMs) has driven a paradigm shift in multimodal fake news detection (MFND), transforming it from traditional…
TimeGNN-Augmented Hybrid-Action MARL for Fine-Grained Task Partitioning and Energy-Aware Offloading in MEC
Wei Ai, Yun Peng, Yuntao Shou +2
With the rapid growth of IoT devices and latency-sensitive applications, the demand for both real-time and energy-efficient computing has surged, placing significant pressure on tr…
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…
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…
GroupFace: Imbalanced Age Estimation Based on Multi-hop Attention Graph Convolutional Network and Group-aware Margin Optimization
Yiping Zhang, Yuntao Shou, Wei Ai +2
With the recent advances in computer vision, age estimation has significantly improved in overall accuracy. However, owing to the most common methods do not take into account the c…
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…
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification
Wei Ai, Jianbin Li, Ze Wang +4
Graph contrastive learning has been successfully applied in text classification due to its remarkable ability for self-supervised node representation learning. However, explicit gr…
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…
Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification
Wei Ai, Jianbin Li, Ze Wang +4
Graph contrastive learning (GCL) has been widely applied to text classification tasks due to its ability to generate self-supervised signals from unlabeled data, thus facilitating…
MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment
Wei Ai, Wen Deng, Hongyi Chen +3
Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems. Existing methods often focus on…
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations
Yuntao Shou, Wei Ai, Jiayi Du +3
The task of multi-modal emotion recognition in conversation (MERC) aims to analyze the genuine emotional state of each utterance based on the multi-modal information in the convers…
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation
Tao Meng, Fuchen Zhang, Yuntao Shou +3
Since Multimodal Emotion Recognition in Conversation (MERC) can be applied to public opinion monitoring, intelligent dialogue robots, and other fields, it has received extensive re…
A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation
Yiping Zhang, Yuntao Shou, Tao Meng +2
The age estimation task aims to use facial features to predict the age of people and is widely used in public security, marketing, identification, and other fields. However, the fe…