2 papers
cs.CV2026
Personalize Your Large Vision-language Models With In-context Prompt Tuning
Yanshu Li, Jiaqian Li, Kuai Yu +4
Large vision-language models (LVLMs) have demonstrated strong general multimodal capability and are increasingly deployed in downstream systems. This trend has driven growing inter…
cs.MM2025
Mitigating Image Captioning Hallucinations in Vision-Language Models
Fei Zhao, Chengcui Zhang, Runlin Zhang +2
Hallucinations in vision-language models (VLMs) hinder reliability and real-world applicability, usually stemming from distribution shifts between pretraining data and test samples…