76 citations · 76 across the 11 of their papers we have counts for
Showing 2026 · cs.CVShow all
3 papers · 2 filters
cs.CV2026
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…
cs.CV2026
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…
cs.CV2026
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…