How Well Can Vision Language Models See Image Details?

• URL: http://arxiv.org/abs/2408.03940v1
• Date: Wed, 7 Aug 2024 17:59:40 GMT
• Title: How Well Can Vision Language Models See Image Details?
• Authors: Chenhui Gou, Abdulwahab Felemban, Faizan Farooq Khan, Deyao Zhu, Jianfei Cai, Hamid Rezatofighi, Mohamed Elhoseiny,
• Abstract summary: We introduce a pixel value prediction task to explore "How Well Can Vision Language Models See Image Details?" Our research reveals that incorporating pixel value prediction as one of the VLM pre-training tasks and vision encoder adaptation markedly boosts VLM performance on downstream image-language understanding tasks.
• Score: 53.036922527685064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model-based Vision-Language Models (LLM-based VLMs) have demonstrated impressive results in various vision-language understanding tasks. However, how well these VLMs can see image detail beyond the semantic level remains unclear. In our study, we introduce a pixel value prediction task (PVP) to explore "How Well Can Vision Language Models See Image Details?" and to assist VLMs in perceiving more details. Typically, these models comprise a frozen CLIP visual encoder, a large language model, and a connecting module. After fine-tuning VLMs on the PVP task, we find: 1) existing VLMs struggle to predict precise pixel values by only fine-tuning the connection module and LLM; and 2) prediction precision is significantly improved when the vision encoder is also adapted. Additionally, our research reveals that incorporating pixel value prediction as one of the VLM pre-training tasks and vision encoder adaptation markedly boosts VLM performance on downstream image-language understanding tasks requiring detailed image perception, such as referring image segmentation (with an average +10.19 cIoU improvement) and video game decision making (with average score improvements of +80.34 and +70.54 on two games, respectively).

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