Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models

• URL: http://arxiv.org/abs/2309.04041v2
• Date: Sat, 13 Jan 2024 03:02:12 GMT
• Title: Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
• Authors: Jiaying Lu, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo, Yawen Zhang, Baochen Sun, Carl Yang and Jie Yang
• Abstract summary: Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks. Their constrained semantic grounding ability hinders their application in real-world scenarios. We propose a data-centric enhancement method that aims to improve LVLMs' semantic grounding ability.
• Score: 25.413601452403213
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks. However, a challenge hindering their application in real-world scenarios, particularly regarding safety, robustness, and reliability, is their constrained semantic grounding ability, which pertains to connecting language to the physical-world entities or concepts referenced in images. Therefore, a crucial need arises for a comprehensive study to assess the semantic grounding ability of widely used LVLMs. Despite the significance, sufficient investigation in this direction is currently lacking. Our work bridges this gap by designing a pipeline for generating large-scale evaluation datasets covering fine-grained semantic information, such as color, number, material, etc., along with a thorough assessment of seven popular LVLMs' semantic grounding ability. Results highlight prevalent misgrounding across various aspects and degrees. To address this issue, we propose a data-centric enhancement method that aims to improve LVLMs' semantic grounding ability through multimodal instruction tuning on fine-grained conversations. Experiments on enhanced LVLMs demonstrate notable improvements in addressing misgrounding issues.

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