Summary of Vimts: a Unified Video and Image Text Spotter For Enhancing the Cross-domain Generalization, by Yuliang Liu et al.
VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization
by Yuliang Liu, Mingxin Huang, Hao Yan, Linger Deng, Weijia Wu, Hao Lu, Chunhua Shen, Lianwen Jin, Xiang Bai
First submitted to arxiv on: 30 Apr 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 presents a novel method called VimTS, which enhances the generalization ability of text spotting models across different domains. The approach involves converting a single-task model into a multi-task model using a Prompt Queries Generation Module and a Tasks-aware Adapter. This enables the model to learn from both image and video scenarios with minimal additional parameters. The authors also propose a synthetic video text dataset (VTD-368k) generated using the Content Deformation Fields (CoDeF) algorithm. Experimental results show that VimTS outperforms the state-of-the-art method by an average of 2.6% in six cross-domain benchmarks. Moreover, VimTS surpasses existing end-to-end video spotting methods on the MOTA metric while requiring significantly fewer parameters and data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to teach computers to find text in pictures or videos from different places. The method is called VimTS and it helps the computer learn from both images and videos at the same time, with minimal extra work. The authors also created a big dataset of video text that they used to test their idea. They found that their approach was better than existing methods by an average of 2.6%. This could be useful for applications like searching for specific information in videos or finding text in old movies. |
Keywords
» Artificial intelligence » Generalization » Multi task » Prompt
