通过分层自训练，逐步适应文本识别的域间差异。
Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition
- 按样本与源/目标域的接近度分层数据，实现渐进式适应。
- 在多个基准数据集上，性能显著优于基线模型。
- 适合处理源域与目标域差异大的场景文本识别任务。
无监督域适应（UDA）在场景文本识别（STR）中日益重要，尤其当训练与测试数据分布不同时。现有方法在源域与目标域差异较大时性能下降明显。本文提出分层域适应（StrDA）方法，通过评估每个样本与源域和目标域的接近度，将训练数据分层，使自训练模型能逐步适应域间变化。我们引入新型域判别器，估计样本的分布外程度与域判别性。在多个基准场景文本数据集上的大量实验表明，该方法显著提升了基线（源域训练）模型的性能。
原文摘要 · Abstract (English)
Unsupervised domain adaptation (UDA) has become increasingly prevalent in scene text recognition (STR), especially where training and testing data reside in different domains. The efficacy of existing UDA approaches tends to degrade when there is a large gap between the source and target domains. To deal with this problem, gradually shifting or progressively learning to shift from domain to domain is the key issue. In this paper, we introduce the Stratified Domain Adaptation (StrDA) approach, which examines the gradual escalation of the domain gap for the learning process. The objective is to partition the training data into subsets so that the progressively self-trained model can adapt to gradual changes. We stratify the training data by evaluating the proximity of each data sample to both the source and target domains. We propose a novel method for employing domain discriminators to estimate the out-of-distribution and domain discriminative levels of data samples. Extensive experiments on benchmark scene-text datasets show that our approach significantly improves the performance of baseline (source-trained) STR models.
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