  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.
