Summary of Ritual: Random Image Transformations As a Universal Anti-hallucination Lever in Large Vision Language Models, by Sangmin Woo et al.
RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models
by Sangmin Woo, Jaehyuk Jang, Donguk Kim, Yubin Choi, Changick Kim
First submitted to arxiv on: 28 May 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 This paper proposes two novel methods, RITUAL and its extension RITUAL+, to reduce hallucinations in Large Vision Language Models (LVLMs). The authors suggest that hallucinations occur when the model misinterprets visual information, leading to unreliable outputs. To address this issue, they leverage randomly transformed images as complementary inputs during decoding, adjusting the output probability distribution without additional training or external models. Specifically, RITUAL uses random transformations to expose the model to diverse visual perspectives, enabling it to correct misinterpretations that lead to hallucinations. In contrast, RITUAL+ selects image transformations based on self-feedback from the LVLM, evaluating and choosing transformations that are most beneficial for reducing hallucinations in a given context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding pictures. Right now, these machines can get confused and make up things that aren’t really there. The researchers want to fix this problem by giving the computer extra information to help it figure out what’s real and what’s not. They do this by changing the picture in different ways, like turning it upside down or making it bigger. This helps the computer understand that some things might look different depending on how you look at them. The researchers also came up with a special way for the computer to decide which changes are most helpful. They tested their ideas and found that they worked really well. |
Keywords
» Artificial intelligence » Probability
