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}.
翻译:将自然场景中的文本确定为本地化是计算机愿景中的一项根本挑战,然而,由于实际场景中文本实例的比例和规模各异,大多数常规文本检测器都存在子文本问题,而次文本问题只是将文本实例的碎片(即子文本)本地化;在这项工作中,我们从数量上分析次文本问题,提出一个简单而有效的设计,即COntransive relation(CORE)模块,以缓解这一问题。CORE首先利用香草关系块来模拟所有文本提案(多个文本实例的子文本)之间的关系,并以对比方式通过实例一级的子文本歧视进一步加强关联性推理。这种方式自然地学习了文本提案的外观表达方式,从而便利了现场文本的探测。我们把CORE模块纳入了M-CNN的两阶段文本探测器,并设计了我们的文本探测器 CORE-Text。关于四种基准的广泛实验显示了CORE-Text的优越性。可以查到代码:\ urls://github.com/jylins/CORERE-EXT}。