FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphs

• URL: http://arxiv.org/abs/2409.13612v1
• Date: Fri, 20 Sep 2024 16:19:53 GMT
• Title: FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphs
• Authors: Bowen Yan, Zhengsong Zhang, Liqiang Jing, Eftekhar Hossain, Xinya Du,
• Abstract summary: We introduce the FIHA (autonomous Fine-graIned Hallucination evAluation evaluation in LVLMs) FIHA could access hallucination LVLMs in the LLM-free and annotation-free way and model the dependency between different types of hallucinations. We introduce a benchmark called FIHA-v1, which consists of diverse questions on various images from MSCOCO and Foggy.
• Score: 12.533011020126855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of Large Vision-Language Models (LVLMs) often comes with widespread hallucination issues, making cost-effective and comprehensive assessments increasingly vital. Current approaches mainly rely on costly annotations and are not comprehensive -- in terms of evaluating all aspects such as relations, attributes, and dependencies between aspects. Therefore, we introduce the FIHA (autonomous Fine-graIned Hallucination evAluation evaluation in LVLMs), which could access hallucination LVLMs in the LLM-free and annotation-free way and model the dependency between different types of hallucinations. FIHA can generate Q&A pairs on any image dataset at minimal cost, enabling hallucination assessment from both image and caption. Based on this approach, we introduce a benchmark called FIHA-v1, which consists of diverse questions on various images from MSCOCO and Foggy. Furthermore, we use the Davidson Scene Graph (DSG) to organize the structure among Q&A pairs, in which we can increase the reliability of the evaluation. We evaluate representative models using FIHA-v1, highlighting their limitations and challenges. We released our code and data.

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