Summary of Look, Compare, Decide: Alleviating Hallucination in Large Vision-language Models Via Multi-view Multi-path Reasoning, by Xiaoye Qu et al.
Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning
by Xiaoye Qu, Jiashuo Sun, Wei Wei, Yu Cheng
First submitted to arxiv on: 30 Aug 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 Recently, Large Vision-Language Models (LVLMs) have showcased impressive capabilities in multi-modal context comprehension. However, they still struggle with hallucination problems, generating inconsistent outputs that deviate from the image content. To address this issue, previous studies primarily focus on retraining LVLMs with custom datasets. Although effective, these methods inherently incur additional computational costs. In this paper, the authors propose a training-free framework called MVP (Multi-View Multi-Path Reasoning) to reduce hallucinations by leveraging the innate capabilities of LVLMs. The approach involves a multi-view information-seeking strategy to comprehensively perceive image information and a multi-path reasoning mechanism for answer decoding that considers the certainty scores of potential answers. Experimental results demonstrate that MVP significantly mitigates the hallucination problem across four well-known LVLMs. The proposed method has been implemented in open-source code available at GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a superpowerful computer program that can understand and answer questions about images! Large Vision-Language Models (LVLMs) are getting better at this, but sometimes they make mistakes and give answers that don’t match the image. To fix this, researchers have come up with a new way to train these programs called MVP (Multi-View Multi-Path Reasoning). Instead of retraining the entire program, MVP makes the most of what the program can do by looking at images in different ways and considering how certain it is about its answers. This new approach works really well and reduces mistakes when answering questions about images. |
Keywords
» Artificial intelligence » Hallucination » Multi modal
