GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition

Fangneng Zhan, Chuhui Xue, Shijian Lu; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 9105-9115

Abstract


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 structure is designed which can convert 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 and greatly improves the concurrent learning in both appearance and geometry spaces. 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhan_2019_ICCV,
author = {Zhan, Fangneng and Xue, Chuhui and Lu, Shijian},
title = {GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}