Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance

Understand

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 to rectify the outputs of LVLMs.
• However, these approaches require either costly training or fine-tuning, or API access to proprietary LLMs for post-generation correction.
• In response to these limitations, we propose Mitigating hallucinAtion via image-gRounded guIdaNcE (MARINE), a framework that is both training-free and API-free.