UT Austin and AWS AI researchers introduce ViGoR, a novel framework utilizing fine-grained reward modeling to enhance LVLMs’ visual grounding. ViGoR considerably improves efficiency and accuracy, outperforming existing models across benchmarks. The innovative framework also includes a comprehensive dataset for evaluation and plans to release a human annotation dataset. Read the full paper for more details.
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ViGoR: Enhancing Visual Grounding of LVLMs
Introduction
Integrating natural language understanding with image perception has led to the development of large vision language models (LVLMs), which showcase remarkable reasoning capabilities. However, LVLMs often encounter challenges in accurately anchoring generated text to visual inputs, resulting in inaccuracies like hallucinations of non-existent scene elements or misinterpretations of object attributes and relationships.
The Solution: ViGoR
Researchers from The University of Texas at Austin and AWS AI propose the innovative framework ViGoR (Visual Grounding Through Fine-Grained Reward Modeling) as a solution. ViGoR advances the visual grounding of LVLMs beyond traditional baselines through fine-grained reward modeling, engaging both human evaluations and automated methods for enhancement. This approach is notably efficient, clarifying the extensive costs of comprehensive supervision typically required in such advancements.
Methodology and Efficacy
ViGoR’s methodology involves strategic fine-tuning of pre-trained LVLMs, such as LLaVA, by introducing a series of images accompanied by prompts to the LVLM. Human annotators then assess these image-text pairs, assigning detailed, sentence-level scores based on the textual quality. This process cultivates a dataset encompassing image-text-evaluation triads. Subsequently, a reward model trained on this dataset refines the LVLM, significantly bolstering its visual grounding capabilities with a relatively modest dataset of 16,000 samples.
ViGoR also integrates an automated method to construct the reward model without additional human labor, further enhancing the visual grounding efficacy of LVLMs. The synergy between human-evaluated and automated reward models underpins ViGoR’s comprehensive solution, markedly improving LVLM performance in accurately grounding text in visual stimuli.
Key Features and Benefits
- Introduces a broadly applicable framework utilizing fine-grained reward modeling to substantially enhance the visual grounding of LVLMs.
- Develops reward models requiring minimal human effort, showcasing significant improvements in visual grounding efficiency.
- Constructs a comprehensive and challenging dataset, MMViG, specifically designed to assess the visual grounding capabilities of LVLMs.
- Plans to release a human evaluation dataset featuring 16K images and generated text pairs with detailed evaluations, enriching resources for related research endeavors.
Conclusion
ViGoR presents a significant advancement in improving LVLMs’ visual grounding accuracy, moving closer to models that understand and describe visual content with high fidelity and detail.
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