  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}.
