Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models

• URL: http://arxiv.org/abs/2404.17534v1
• Date: Fri, 26 Apr 2024 16:59:26 GMT
• Title: Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models
• Authors: Yuhang Huang, Zihan Wu, Chongyang Gao, Jiawei Peng, Xu Yang,
• Abstract summary: We study how models like Open-Flamingo, IDEFICS, and MiniGPT-4 can distinguish between similar objects and accurately describe visual features. We propose the Textual Retrieval-Augmented Classification (TRAC) framework, which allows us to delve deeper into analyzing fine-grained visual description generation.
• Score: 16.524244395901356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, by leveraging its generative capabilities, allows us to delve deeper into analyzing fine-grained visual description generation. This research provides valuable insights into the generation quality of LVLMs, enhancing the understanding of multimodal language models. Notably, MiniGPT-4 stands out for its better ability to generate fine-grained descriptions, outperforming the other two models in this aspect. The code is provided at \url{https://anonymous.4open.science/r/Explore_FGVDs-E277}.

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