HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding

• URL: http://arxiv.org/abs/2403.00425v2
• Date: Mon, 10 Jun 2024 15:21:41 GMT
• Title: HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
• Authors: Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, Jiawei Zhou,
• Abstract summary: HALC is a novel decoding algorithm designed to mitigate object hallucinations (OH) in large vision-language models (LVLMs) HALC integrates a robust auto-focal grounding mechanism (locally) to correct hallucinated tokens on the fly, and a specialized beam search algorithm (globally) to significantly reduce OH.
• Score: 30.30494071474536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large vision-language models (LVLMs) have demonstrated impressive capabilities in interpreting multi-modal contexts, they invariably suffer from object hallucinations (OH). We introduce HALC, a novel decoding algorithm designed to mitigate OH in LVLMs. HALC leverages distinct fine-grained optimal visual information in vision-language tasks and operates on both local and global contexts simultaneously. Specifically, HALC integrates a robust auto-focal grounding mechanism (locally) to correct hallucinated tokens on the fly, and a specialized beam search algorithm (globally) to significantly reduce OH while preserving text generation quality. Additionally, HALC can be integrated into any LVLMs as a plug-and-play module without extra training. Extensive experimental studies demonstrate the effectiveness of HALC in reducing OH, outperforming state-of-the-arts across four benchmarks.

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