3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…