Recent adversarial learning research has achieved very impressive progress
for modelling cross-domain data shifts in appearance space but its counterpart
in modelling cross-domain shifts in geometry space lags far behind. This paper
presents an innovative Geometry-Aware Domain Adaptation Network (GA-DAN) that
is capable of modelling cross-domain shifts concurrently in both geometry space
and appearance space and realistically converting images across domains with
very different characteristics. In the proposed GA-DAN, a novel multi-modal
spatial learning technique is designed which converts a source-domain image
into multiple images of different spatial views as in the target domain. A new
disentangled cycle-consistency loss is introduced which balances the cycle
consistency in appearance and geometry spaces and improves the learning of the
whole network greatly. The proposed GA-DAN has been evaluated for the classic
scene text detection and recognition tasks, and experiments show that the
domain-adapted images achieve superior scene text detection and recognition
performance while applied to network training.

本文提出了一种新颖的能够同时模拟几何空间和出现空间中的跨域移位的几何感知域自适应网络 (GA-DAN)，并可在具有非常不同特征的不同领域之间实现逼真的图像转换。在所提出的 GA-DAN 中，设计了一种新颖的多模态空间学习技术，它将源域图像转换为目标域中不同空间视图的多个图像。还引入了一种新的分离循环一致性损失 (loss)，来平衡外观空间和几何空间的循环一致性，并大大提高整个网络的学习性能，该 GA-DAN 在经典场景文本检测和识别任务中进行了评估，实验证明，在应用于网络训练时，域适应图像可以实现更好的场景文本检测和识别性能。