Summary of Focus, Distinguish, and Prompt: Unleashing Clip For Efficient and Flexible Scene Text Retrieval, by Gangyan Zeng et al.
Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval
by Gangyan Zeng, Yuan Zhang, Jin Wei, Dongbao Yang, Peng Zhang, Yiwen Gao, Xugong Qin, Yu Zhou
First submitted to arxiv on: 1 Aug 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 proposes a novel approach to scene text retrieval, which aims to find all images containing the query text from an image gallery. Unlike current efforts that rely on Optical Character Recognition (OCR) pipelines, this work leverages Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. The proposed model, FDP (Focus, Distinguish, and Prompt), addresses two key challenges: limited text perceptual scale and entangled visual-semantic concepts. FDP first focuses on scene text by shifting attention to the text area and probing hidden text knowledge, then divides the query text into content words and function words for processing. Experimental results show that FDP achieves better or competitive retrieval accuracy compared to existing methods while significantly improving inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about helping computers find pictures with specific text in them. Right now, people are using special software to recognize the text first, which takes a lot of work and isn’t very flexible. This new approach uses a different way called Contrastive Language-Image Pre-training (CLIP) that doesn’t need this extra step. The team came up with a new model called FDP that helps it find the right pictures faster and more accurately. They tested it on lots of different texts and showed that it can do better than other methods. |
Keywords
» Artificial intelligence » Attention » Inference » Prompt
