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Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models
April 29, 2024, 4:45 a.m. | Yuhang Huang, Zihan Wu, Chongyang Gao, Jiawei Peng, Xu Yang
cs.CV updates on arXiv.org arxiv.org
Abstract: Large Vision-Language Models (LVLMs) are gaining traction for their remarkable ability to process and integrate visual and textual data. Despite their popularity, the capacity of LVLMs to generate precise, fine-grained textual descriptions has not been fully explored. This study addresses this gap by focusing on \textit{distinctiveness} and \textit{fidelity}, assessing how models like Open-Flamingo, IDEFICS, and MiniGPT-4 can distinguish between similar objects and accurately describe visual features. We proposed the Textual Retrieval-Augmented Classification (TRAC) framework, which, …
arxiv cs.cv cs.mm fidelity generated language language models type vision vision-language vision-language models
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