Summary of Unaligning Everything: or Aligning Any Text to Any Image in Multimodal Models, by Shaeke Salman et al.
Unaligning Everything: Or Aligning Any Text to Any Image in Multimodal Models
by Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 research paper proposes an innovative approach to matching text embeddings with images, showcasing unprecedented zero-shot capabilities in multimodal models. The study extends a recent gradient-based procedure to align text embeddings with images, demonstrating how unnoticeable attacks can manipulate joint image-text models. The results reveal that semantically unrelated images can have identical text embeddings and visually indistinguishable images can be matched to very different text embeddings. The technique achieves 100% success rate on text datasets and images from multiple sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to match a picture with the right words. This paper shows how, using special techniques, we can align images with texts in ways that seem magical! The researchers found that if someone wants to trick a machine learning model into thinking one image is another, they can do it by very slightly modifying an image. This means that text and image models are not as secure as thought. |
Keywords
» Artificial intelligence » Machine learning » Zero shot
