ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling

• URL: http://arxiv.org/abs/2402.06118v2
• Date: Thu, 18 Apr 2024 04:33:53 GMT
• Title: ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
• Authors: Siming Yan, Min Bai, Weifeng Chen, Xiong Zhou, Qixing Huang, Li Erran Li,
• Abstract summary: Recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities. The generated text often suffers from inaccurate grounding in the visual input, resulting in errors such as hallucination of nonexistent scene elements. We introduce a novel framework, ViGoR, that utilizes fine-grained reward modeling to significantly enhance the visual grounding of LVLMs over pre-trained baselines.
• Score: 35.098725056881655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities. However, the generated text often suffers from inaccurate grounding in the visual input, resulting in errors such as hallucination of nonexistent scene elements, missing significant parts of the scene, and inferring incorrect attributes of and relationships between objects. To address these issues, we introduce a novel framework, ViGoR(Visual Grounding Through Fine-Grained Reward Modeling) that utilizes fine-grained reward modeling to significantly enhance the visual grounding of LVLMs over pre-trained baselines. This improvement is efficiently achieved using much cheaper human evaluations instead of full supervisions, as well as automated methods. We show the effectiveness of our approach through a variety of evaluation methods and benchmarks. Additionally, we plan to release our human annotation comprising approximately 16,000 images and generated text pairs with fine-grained evaluations to contribute to related research in the community.

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