HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
<|reference_start|>HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding: 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.<|reference_end|>
@article{chen2024halc:,
    title={HALC: Object Hallucination Reduction via Adaptive Focal-Contrast
  Decoding},
    author={Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, Jiawei
  Zhou},
    journal={arXiv preprint arXiv:2403.00425},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00425},
    primaryClass={cs.CV cs.AI cs.LG}
}