76 citations · 76 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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,…