Summary of Region Prompt Tuning: Fine-grained Scene Text Detection Utilizing Region Text Prompt, by Xingtao Lin et al.
Region Prompt Tuning: Fine-grained Scene Text Detection Utilizing Region Text Prompt
by Xingtao Lin, Heqian Qiu, Lanxiao Wang, Ruihang Wang, Linfeng Xu, Hongliang Li
First submitted to arxiv on: 20 Sep 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 The proposed region prompt tuning (RPT) method successfully adapts large-scale models like Contrastive Language-Image Pre-trained (CLIP) for fine-grained scene text detection. Traditional text prompts often neglect fine-grained details, leading to the omission of detailed features and fine-grained text in scene text detection tasks. RPT addresses this limitation by decomposing region text prompts into individual characters and visual feature maps into region tokens, creating a one-to-one correspondence between characters and tokens. This allows for character-token level interactions before and after encoding, refining information at the fine-grained level. The method combines general score maps from image-text processes with region score maps derived from character-token matching to produce a final score map that balances global and local features. This approach is effective for scene text detection, as demonstrated by experiments on benchmarks like ICDAR2015, TotalText, and CTW1500. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scene text detection helps machines read text in images! Recently, huge language models like CLIP have been used for this task, but they often miss important details. This new method, called RPT, tries to fix this by breaking down the image into smaller parts (like individual characters) and matching them with tiny pieces of visual information. It’s like a game of ” Simon Says” where each character says what it sees in the image! The approach is very good at finding text in scenes, as shown on famous benchmark tests. |
Keywords
» Artificial intelligence » Prompt » Token
