Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance

• URL: http://arxiv.org/abs/2402.08680v1
• Date: Tue, 13 Feb 2024 18:59:05 GMT
• Title: Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance
• Authors: Linxi Zhao and Yihe Deng and Weitong Zhang and Quanquan Gu
• Abstract summary: Large Vision-Language Models (LVLMs) tend to hallucinate non-existing objects in the images. We introduce a framework called Mitigating hallucinAtion via classifieR-Free guIdaNcE (MARINE) MARINE is both training-free and API-free, and can effectively and efficiently reduce object hallucinations during the generation process.
• Score: 56.04768229686853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advancement of Large Vision-Language Models (LVLMs) has increasingly highlighted the critical issue of their tendency to hallucinate non-existing objects in the images. To address this issue, previous works focused on using specially curated datasets or powerful LLMs (e.g., GPT-3.5) to rectify the outputs of LVLMs. However, these approaches require either expensive training/fine-tuning or API access to advanced LLMs to correct the model's output post-generation. In this paper, we tackle this challenge by introducing a framework called Mitigating hallucinAtion via classifieR-Free guIdaNcE (MARINE), which is both training-free and API-free, and can effectively and efficiently reduce object hallucinations during the generation process. Specifically, MARINE enriches the visual context of LVLMs by integrating existing open-source vision models, and employs classifier-free guidance to incorporate the additional object grounding features to improve the precision of LVLMs' generations. Through comprehensive evaluations across $6$ popular LVLMs with diverse evaluation metrics, we demonstrate the effectiveness of MARINE, which even outperforms existing fine-tuning-based methods. Remarkably, it not only reduces hallucinations but also improves the detailedness of LVLMs' generations, as assessed by GPT-4V.

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