Summary of Was: Dataset and Methods For Artistic Text Segmentation, by Xudong Xie et al.
WAS: Dataset and Methods for Artistic Text Segmentation
by Xudong Xie, Yuzhe Li, Yang Liu, Zhifei Zhang, Zhaowen Wang, Wei Xiong, Xiang Bai
First submitted to arxiv on: 31 Jul 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 A novel approach to artistic text segmentation is presented in this paper, which tackles the challenge of segmenting complex and diverse stroke shapes. The authors propose a decoder with layer-wise momentum queries to prevent model bias towards specific stroke regions. Additionally, they design a skeleton-assisted head to guide the model towards the global topological structure. To enhance generalization performance, they introduce a data synthesis strategy based on large multi-modal models and diffusion models. Experimental results demonstrate that their proposed method and synthetic dataset outperform existing approaches on artistic text segmentation tasks and achieve state-of-the-art results on public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to separate words in artistic texts, like handwritten notes or illustrations. Currently, computers are not very good at this task because the shapes of letters and words can be very different and complex. The authors propose a new model that includes two main parts: one that helps with recognizing specific letter shapes and another that understands the overall structure of the text. They also introduce a way to generate more training data by mixing and matching existing images. This approach improves the computer’s ability to separate words in artistic texts, achieving better results than previous methods. |
Keywords
» Artificial intelligence » Decoder » Generalization » Multi modal
