Summary of Vacode: Visual Augmented Contrastive Decoding, by Sihyeon Kim et al.
VACoDe: Visual Augmented Contrastive Decoding
by Sihyeon Kim, Boryeong Cho, Sangmin Bae, Sumyeong Ahn, Se-Young Yun
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent Large Vision-Language Models (LVLMs) have achieved impressive performance, but often generate inaccurate responses due to hallucinations. To address this issue, researchers have employed contrastive decoding (CD) with augmented images, which amplifies the contrast with the original image. However, these methods rely on a single augmentation, which is restrictive for certain tasks and requires external knowledge. This study explores using multiple image augmentations to mitigate hallucinations. The authors observe that different augmentations produce varying levels of contrast depending on the task and introduce a novel method called VACoDe, Visual Augmented Contrastive Decoding. VACoDe adaptively selects the augmentation with the highest contrast for each task using a softmax distance metric. Experimental results show that VACoDe outperforms previous methods and improves output quality in various vision-language tasks, including its universality across different model types and sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Models (LVLMs) are really smart, but sometimes they make mistakes by creating fake information. To fix this, some researchers used a technique called contrastive decoding with special images to make the mistake worse. This makes it harder for them to make mistakes in the first place! But this method only works if you use one type of image and has its own limitations. In this study, scientists looked into using different types of images to help LVLMs be more accurate. They found that different types of images work better or worse depending on what task they’re trying to do. Then, they created a new way to pick the best type of image for each task called VACoDe. This method is really good at making LVLMs produce better results and works with all kinds of models. |
Keywords
» Artificial intelligence » Softmax
