Summary of Delve Into Visual Contrastive Decoding For Hallucination Mitigation Of Large Vision-language Models, by Yi-lun Lee et al.
Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models
by Yi-Lun Lee, Yi-Hsuan Tsai, Wei-Chen Chiu
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Medium Difficulty summary: This paper investigates how large vision-language models (LVLMs) can be improved to reduce hallucinations, where generated text inaccurately reflects visual contents. To address this issue, recent approaches use contrastive decoding by calibrating the model’s response via contrasting output distributions with original and visually distorted samples. The proposed methods for contrastive decoding include image downsampling and editing, which change information in visual inputs. The paper analyzes probability-level metrics such as entropy and distribution distance to study the effects of different contrastive samples on hallucination mitigation across various LVLMs and benchmarks. The results show that the effect of these samples varies significantly, leading to the proposal of a simple yet effective method for combining contrastive samples. This fusion method is validated through extensive experiments across multiple benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a computer program that can generate text based on what it sees in an image. While this technology has made great progress, there’s still a problem – sometimes the generated text doesn’t accurately reflect what’s in the image. To solve this issue, researchers are experimenting with different ways to “calibrate” the program by showing it distorted versions of the original image. This paper explores two new methods for calibrating the program: making the image less detailed and changing the contents of the image entirely. By analyzing how well these methods work across different programs and scenarios, the authors propose a simple way to combine the best features of each method. The results show that this combined approach is effective in reducing errors and improving the overall performance of the program. |
Keywords
» Artificial intelligence » Hallucination » Probability
