12 citations · 59 across the 37 of their papers we have counts for
14 papers · 1 filter
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…
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck
Yuntao Shou, Haozhi Lan, Xiangyong Cao
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer…
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…
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…