Summary of Enhancing Image Retrieval : a Comprehensive Study on Photo Search Using the Clip Mode, by Naresh Kumar Lahajal and Harini S
Enhancing Image Retrieval : A Comprehensive Study on Photo Search using the CLIP Mode
by Naresh Kumar Lahajal, Harini S
First submitted to arxiv on: 24 Jan 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 introduces Contrastive Language-Image Pretraining (CLIP), a powerful model that enables cross-modal understanding between images and text. By learning a shared representation space through pre-training on a large-scale dataset, CLIP demonstrates exceptional generalization capabilities for tasks like zero-shot learning and few-shot classification. The model’s ability to understand semantic relationships between diverse image and text pairs makes it an efficient and accurate tool for photo search, revolutionizing information retrieval in multimedia applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to find the perfect picture of a sunset just by searching for “beautiful beach scene”. This is what CLIP can do! It’s a special kind of computer model that helps computers understand images and words. By training on lots of pictures and their descriptions, CLIP gets really good at matching words with pictures, even if it hasn’t seen those exact words or pictures before. This means we can use CLIP to search for pictures in new and creative ways, like finding pictures based on what someone describes instead of just typing keywords. |
Keywords
» Artificial intelligence » Classification » Few shot » Generalization » Pretraining » Zero shot
