تقرير
Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors
| العنوان: | Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors |
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| المؤلفون: | Guan, Tongkun, Shen, Wei, Yang, Xue, Wang, Xuehui, Yang, Xiaokang |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Existing scene text detection methods typically rely on extensive real data for training. Due to the lack of annotated real images, recent works have attempted to exploit large-scale labeled synthetic data (LSD) for pre-training text detectors. However, a synth-to-real domain gap emerges, further limiting the performance of text detectors. Differently, in this work, we propose FreeReal, a real-domain-aligned pre-training paradigm that enables the complementary strengths of both LSD and unlabeled real data (URD). Specifically, to bridge real and synthetic worlds for pre-training, a glyph-based mixing mechanism (GlyphMix) is tailored for text images.GlyphMix delineates the character structures of synthetic images and embeds them as graffiti-like units onto real images. Without introducing real domain drift, GlyphMix freely yields real-world images with annotations derived from synthetic labels. Furthermore, when given free fine-grained synthetic labels, GlyphMix can effectively bridge the linguistic domain gap stemming from English-dominated LSD to URD in various languages. Without bells and whistles, FreeReal achieves average gains of 1.97%, 3.90%, 3.85%, and 4.56% in improving the performance of FCENet, PSENet, PANet, and DBNet methods, respectively, consistently outperforming previous pre-training methods by a substantial margin across four public datasets. Code is available at https://github.com/SJTU-DeepVisionLab/FreeReal. Comment: Accepted by ECCV2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.05286 |
| رقم الأكسشن: | edsarx.2312.05286 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |