Summary of Compressible and Searchable: Ai-native Multi-modal Retrieval System with Learned Image Compression, by Jixiang Luo
Compressible and Searchable: AI-native Multi-Modal Retrieval System with Learned Image Compression
by Jixiang Luo
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
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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 a framework that efficiently stores and retrieves digital content across diverse modalities by fusing AI-native multi-modal search capabilities with neural image compression. The approach analyzes the relationship between compressibility and searchability, recognizing their pivotal roles in storage and retrieval systems. A simple adapter bridges the features of Learned Image Compression (LIC) and Contrastive Language-Image Pretraining (CLIP), retaining semantic fidelity and retrieval accuracy for multi-modal data. Experimental evaluations on Kodak datasets demonstrate significant enhancements in compression efficiency and search accuracy compared to existing methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem – how to store and find lots of different types of digital content quickly and efficiently. Right now, computers struggle to keep up with the amount of data being created. To fix this, researchers combined two powerful AI tools: one that compresses images and another that matches words with pictures. They made a special “adapter” that lets these two tools work together seamlessly. This helps ensure that digital content is stored in a way that makes it easy to find later. Tests showed that their approach works much better than current methods. |
Keywords
» Artificial intelligence » Multi modal » Pretraining
