76 citations · 76 across the 11 of their papers we have counts for
12 papers
Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models
Yang Chen, Zhan Zhuang, Yanbin Wei +3
While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generalization overfitting on seen clas…
VizRAG: Enhancing Retrieval-Augmented Generation with Hypergraph Visualization
Yanbin Wei, Yang Chen, Renling Gan +7
Hypergraph-based RAG systems surpass traditional graph-based approaches by organizing complex n-ary atomic facts among entities, rather than relying solely on binary relationships.…
HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
Yanbin Wei, Chun Kang, Siwei Li +11
Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain unexplored. In the real wor…
Graph2Video: Leveraging Video Models to Model Dynamic Graph Evolution
Hua Liu, Yanbin Wei, Fei Xing +3
Dynamic graphs are common in real-world systems such as social media, recommender systems, and traffic networks. Existing dynamic graph models for link prediction often fall short…
DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs
Yanbin Wei, Jiangyue Yan, Chun Kang +4
Vision-Language Models (VLMs) have emerged as versatile solutions for zero-shot question answering (QA) across various domains. However, enabling VLMs to effectively comprehend str…
Alternating Training-based Label Smoothing Enhances Prompt Generalization
Yang Chen, Yanbin Wei, Ke Jin +3
Recent advances in pre-trained vision-language models have demonstrated remarkable zero-shot generalization capabilities. To further enhance these models' adaptability to various d…
Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering
Yanbin Wei, Jiangyue Yan, Chun Kang +4
Large Multimodal Models (LMMs) have shown generalized zero-shot capabilities in diverse domain question-answering (QA) tasks, including graph QA that involves complex graph topolog…
Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation
Zhan Zhuang, Xiequn Wang, Wei Li +9
Low-rank adaptation (LoRA) has emerged as a leading parameter-efficient fine-tuning technique for adapting large foundation models, yet it often locks adapters into suboptimal mini…
DynamicMind: A Tri-Mode Thinking System for Large Language Models
Wei Li, Yanbin Wei, Qiushi Huang +4
Modern large language models (LLMs) often struggle to dynamically adapt their reasoning depth to varying task complexities, leading to suboptimal performance or inefficient resourc…
Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction
Yanbin Wei, Xuehao Wang, Zhan Zhuang +5
Message-passing graph neural networks (MPNNs) and structural features (SFs) are cornerstones for the link prediction task. However, as a common and intuitive mode of understanding,…
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
Yanbin Wei, Qiushi Huang, James T. Kwok +1
Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC. They can b…
GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning
Yanbin Wei, Shuai Fu, Weisen Jiang +5
Large Language Models (LLMs) are increasingly used for various tasks with graph structures. Though LLMs can process graph information in a textual format, they overlook the rich vi…