A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models

• URL: http://arxiv.org/abs/2505.01958v1
• Date: Sun, 04 May 2025 01:47:58 GMT
• Title: A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
• Authors: Liqiang Jing, Guiming Hardy Chen, Ehsan Aghazadeh, Xin Eric Wang, Xinya Du,
• Abstract summary: Vision-Language Models (LVLMs) demonstrate remarkable capabilities in multimodal tasks.<n>LVLMs generate inaccurate visual object-related information based on the query input, potentially leading to misinformation and concerns about safety and reliability.<n>In this paper, we analyze each component of LLaVA-like LVLMs to identify potential sources of error and their impact.
• Score: 30.037505914306504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities in multimodal tasks, but visual object hallucination remains a persistent issue. It refers to scenarios where models generate inaccurate visual object-related information based on the query input, potentially leading to misinformation and concerns about safety and reliability. Previous works focus on the evaluation and mitigation of visual hallucinations, but the underlying causes have not been comprehensively investigated. In this paper, we analyze each component of LLaVA-like LVLMs -- the large language model, the vision backbone, and the projector -- to identify potential sources of error and their impact. Based on our observations, we propose methods to mitigate hallucination for each problematic component. Additionally, we developed two hallucination benchmarks: QA-VisualGenome, which emphasizes attribute and relation hallucinations, and QA-FB15k, which focuses on cognition-based hallucinations.

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