HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913383475838976 |
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| author | Chen, Zhaorun Zhao, Zhuokai Luo, Hongyin Yao, Huaxiu Li, Bo Zhou, Jiawei |
| author_facet | Chen, Zhaorun Zhao, Zhuokai Luo, Hongyin Yao, Huaxiu Li, Bo Zhou, Jiawei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding Chen, Zhaorun Zhao, Zhuokai Luo, Hongyin Yao, Huaxiu Li, Bo Zhou, Jiawei Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.00425 |