Summary of Investigating and Mitigating Object Hallucinations in Pretrained Vision-language (clip) Models, by Yufang Liu et al.
Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
by Yufang Liu, Tao Ji, Changzhi Sun, Yuanbin Wu, Aimin Zhou
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 This paper investigates object hallucinations within Large Vision-Language Models (LVLMs), specifically the CLIP model, which is widely used for vision-language applications. The researchers reveal that even isolated from language modalities, the CLIP model exhibits object hallucinations, suggesting a broader issue. To mitigate this problem, they propose a counterfactual data augmentation method to generate negative samples with various hallucination issues. This approach effectively reduces object hallucinations in the CLIP model and enables it to be used as a visual encoder for LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Object hallucinations are a serious issue with large vision-language models, but where do they come from? Researchers looked into this problem specifically with the popular CLIP model. They found that even when language isn’t involved, the CLIP model still makes mistakes and imagines things that aren’t there! To fix this, they came up with a new way to add fake data to training that helps reduce these hallucinations. This can make the model better at its job of understanding images. |
Keywords
» Artificial intelligence » Data augmentation » Encoder » Hallucination
