Summary of Halc: Object Hallucination Reduction Via Adaptive Focal-contrast Decoding, by Zhaorun Chen et al.
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
by Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, Jiawei Zhou
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel decoding algorithm, called HALC, is proposed to mitigate object hallucinations (OH) in large vision-language models (LVLMs). This algorithm integrates a robust auto-focal grounding mechanism and a specialized beam search algorithm to correct hallucinated tokens while preserving text generation quality. HALC can be easily integrated into any LVLM without requiring additional training. Experimental results demonstrate the effectiveness of HALC in reducing OH, outperforming state-of-the-art models across four benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers better understand pictures and words is being developed. Right now, these computers sometimes make mistakes when trying to describe what they see. This new method, called HALC, helps fix those mistakes by making sure the computer only talks about things that are really in the picture. It’s like a special filter that makes the computer more accurate. HALC can be used with any of these computers and doesn’t need extra training. The results show that this new method works better than others in four tests. |
Keywords
* Artificial intelligence * Grounding * Text generation
