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Evaluating and Mitigating Relationship Hallucinations in Large Vision-Language Models.

Mingrui Wu, Jiale Li, Jiayi Ji

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    Summary
    This summary is machine-generated.

    Large Vision-Language Models (LVLMs) struggle with relationship hallucinations. A new benchmark, R-Bench, and a method called Region-Aware Alignment Mitigation (RA²M) improve visual relationship understanding by focusing on region-level alignment.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Large Vision-Language Models (LVLMs) exhibit prevalent hallucination issues.
    • Existing research primarily addresses object hallucinations, overlooking inter-object relationship inaccuracies crucial for visual comprehension.

    Purpose of the Study:

    • To introduce R-Bench, a novel benchmark for evaluating relationship hallucinations in LVLMs.
    • To analyze the causes of relationship hallucinations and propose mitigation strategies.

    Main Methods:

    • Developed R-Bench with image-level and instance-level questions to assess visual relationships.
    • Analyzed hallucination causes including relationship-relationship, subject-relationship, and relationship-object co-occurrences, exacerbated by long-tail data distributions.
    • Proposed Region-Aware Alignment Mitigation (RA²M) to enhance region-level image-text alignment.

    Main Results:

    • Identified that LVLMs often disregard visual content, relying excessively on language model common sense, especially in spatial reasoning.
    • Demonstrated that relationship hallucinations stem from specific co-occurrence patterns within visual datasets.
    • Showcased that region-level alignment significantly mitigates relationship hallucinations.

    Conclusions:

    • Relationship hallucinations are a significant challenge in LVLMs, distinct from object hallucinations.
    • R-Bench provides a critical tool for assessing and advancing LVLM capabilities in understanding visual relationships.
    • RA²M offers a promising direction for improving the factual accuracy and visual grounding of LVLMs.