SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding

Woohyeon Park; Woojin Kim; Jaeik Kim; Jaeyoung Do

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arXiv · 2506.08391

SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding

Woohyeon Park·Woojin Kim·Jaeik Kim·Jaeyoung Do

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Abstract

Despite significant advancements in Vision-Language Models (VLMs), the performance of existing VLMs remains hindered by object hallucination, a critical challenge to achieving accurate visual understanding. To address this issue, we propose SECOND: Selective and Contrastive Decoding, a novel approach that enables VLMs to effectively leverage multi-scale visual information with an object-centric manner, closely aligning with human visual perception. SECOND progressively selects and integrates multi-scale visual information, facilitating a more precise interpretation of images. By contrasting these visual information iteratively, SECOND significantly reduces perceptual hallucinations and outperforms a wide range of benchmarks. Our theoretical analysis and experiments highlight the largely unexplored potential of multi-scale application in VLMs, showing that prioritizing and contrasting across scales outperforms existing methods.

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Woohyeon Park, Woojin Kim, Jaeik Kim, Jaeyoung Do. 2025-06-10. SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding. https://arxiv.org/abs/2506.08391

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