24 citations · 52 across the 7 of their papers we have counts for
7 papers
Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning
Zhaoxing Li, Hai-Feng Zhang, Xiaoming Zhang
Conventional Graph Contrastive Learning (GCL) on Text-Attributed Graphs (TAGs) relies on blind stochastic augmentations, inadvertently entangling task-relevant signals with noise.…
Hybrid CNN-Mamba Enhancement Network for Robust Multimodal Sentiment Analysis
Xiang Li, Xianfu Cheng, Xiaoming Zhang +1
Multimodal Sentiment Analysis (MSA) with missing modalities has recently attracted increasing attention. Although existing research mainly focuses on designing complex model archit…
TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis
Xiang Li, Xianfu Cheng, Dezhuang Miao +2
Multimodal Sentiment Analysis (MSA) with missing modalities has attracted increasing attention recently. While current Transformer-based methods leverage dense text information to…
Keyphrase Generation with Correlation Constraints
Jun Chen, Xiaoming Zhang, Yu Wu +2
In this paper, we study automatic keyphrase generation. Although conventional approaches to this task show promising results, they neglect correlation among keyphrases, resulting i…
Learning Social Image Embedding with Deep Multimodal Attention Networks
Feiran Huang, Xiaoming Zhang, Zhoujun Li +3
Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and…
Location Prediction of Social Images via Generative Model
Xiaoming Zhang, Zhoujun Li, Senzhang Wang +2
The vast amount of geo-tagged social images has attracted great attention in research of predicting location using the plentiful content of images, such as visual content and textu…
Future Influence Ranking of Scientific Literature
Senzhang Wang, Sihong Xie, Xiaoming Zhang +3
Researchers or students entering a emerging research area are particularly interested in what newly published papers will be most cited and which young researchers will become infl…