Name
Abstract | ||
|---|---|---|
Localizing text instances in natural scenes is regarded as a fundamental challenge in computer vision. Nevertheless, owing to the extremely varied aspect ratios and scales of text instances in real scenes, most conventional text detectors suffer from the sub-text problem that only localizes the fragments of text instance (i.e., sub-texts). In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue. CORE first leverages a vanilla relation block to model the relations among all text proposals (sub-texts of multiple text instances) and further enhances relational reasoning via instance-level sub-text discrimination in a contrastive manner. Such way naturally learns instance-aware representations of text proposals and thus facilitates scene text detection. We integrate the CORE module into a two-stage text detector of Mask R-CNN and devise our text detector CORE-Text. Extensive experiments on four benchmarks demonstrate the superiority of CORE-Text. Code is available: \url{https://github.com/jylins/CORE-Text}. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICME51207.2021.9428457 | ICME |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyang Lin | 1 | 0 | 0.68 |
| Yingwei Pan | 2 | 357 | 23.66 |
| Rongfeng Lai | 3 | 20 | 2.07 |
| Xuehang Yang | 4 | 0 | 0.34 |
| Hongyang Chao | 5 | 495 | 36.96 |
| Ting Yao | 6 | 3 | 3.10 |