Summary of Magiclens: Self-supervised Image Retrieval with Open-ended Instructions, by Kai Zhang et al.
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
by Kai Zhang, Yi Luan, Hexiang Hu, Kenton Lee, Siyuan Qiao, Wenhu Chen, Yu Su, Ming-Wei Chang
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Multimedia (cs.MM)
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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 The proposed MagicLens framework enables open-ended image retrieval by leveraging text instructions, showcasing improved performance on various benchmarks compared to prior state-of-the-art models. The novel approach capitalizes on implicit relations present in web pages, synthesizing instructions using foundation models. By training the model on 36.7M triplets with rich semantic relations, MagicLens achieves comparable or better results while maintaining high parameter efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to find images based on text instructions. Imagine being able to search for images by giving a description of what you’re looking for, rather than just searching for similar pictures. The authors developed a system called MagicLens that can do this and even improve upon existing methods. They trained their model on millions of examples and tested it on various tasks, showing it works well and is efficient. |
