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

• URL: http://arxiv.org/abs/2506.08391v1
• Date: Tue, 10 Jun 2025 02:55:38 GMT
• Title: SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding
• Authors: Woohyeon Park, Woojin Kim, Jaeik Kim, Jaeyoung Do,
• Abstract summary: SECOND: Selective and Contrastive Decoding is a novel approach that enables Vision-Language Models to leverage multi-scale visual information with an object-centric manner.<n> SECOND significantly reduces perceptual hallucinations and outperforms a wide range of benchmarks.
• Score: 5.976839106353883
• License: http://creativecommons.org/licenses/by/4.0/
• 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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