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

arXiv: https://arxiv.org/abs/1907.09653 · HTML: https://mlsanity.com/paper/1907.09653/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 30 of 37)

- [Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data](https://mlsanity.com/paper/2110.03374/) (2021)
- [Diverse Image Inpainting with Bidirectional and Autoregressive Transformers](https://mlsanity.com/paper/2104.12335/) (2021)
- [Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency](https://mlsanity.com/paper/2107.11355/) (2021)
- [Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection](https://mlsanity.com/paper/2103.00236/) (2021)
- [Multimodal Image Synthesis and Editing: The Generative AI Era](https://mlsanity.com/paper/2112.13592/) (2021)
- [RDA: Robust Domain Adaptation via Fourier Adversarial Attacking](https://mlsanity.com/paper/2106.02874/) (2021)
- [Turning a CLIP Model into a Scene Text Detector](https://mlsanity.com/paper/2302.14338/) (2023)
- [Unbalanced Feature Transport for Exemplar-based Image Translation](https://mlsanity.com/paper/2106.10482/) (2021)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Blind Image Super-Resolution via Contrastive Representation Learning](https://mlsanity.com/paper/2107.00708/) (2021)
- [GMLight: Lighting Estimation via Geometric Distribution Approximation](https://mlsanity.com/paper/2102.10244/) (2021)
- [Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting](https://mlsanity.com/paper/2203.03911/) (2022)
- [Domain Adaptive Video Segmentation via Temporal Consistency Regularization](https://mlsanity.com/paper/2107.11004/) (2021)
- [Bi-level Feature Alignment for Versatile Image Translation and Manipulation](https://mlsanity.com/paper/2107.03021/) (2021)
- [Marginal Contrastive Correspondence for Guided Image Generation](https://mlsanity.com/paper/2204.00442/) (2022)
- [Sparse Needlets for Lighting Estimation with Spherical Transport Loss](https://mlsanity.com/paper/2106.13090/) (2021)
- [Self-Supervised Text Erasing with Controllable Image Synthesis](https://mlsanity.com/paper/2204.12743/) (2022)
- [Auto-regressive Image Synthesis with Integrated Quantization](https://mlsanity.com/paper/2207.10776/) (2022)
- [Turning a CLIP Model into a Scene Text Spotter](https://mlsanity.com/paper/2308.10408/) (2023)
- [Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors](https://mlsanity.com/paper/2312.05286/) (2023)
- [Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting](https://mlsanity.com/paper/2104.03520/) (2021)
- [Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark](https://mlsanity.com/paper/2108.10840/) (2021)
- [FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework](https://mlsanity.com/paper/2308.02905/) (2023)
- [Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter](https://mlsanity.com/paper/2106.05920/) (2021)
- [OmniPrint: A Configurable Printed Character Synthesizer](https://mlsanity.com/paper/2201.06648/) (2022)
- [The Devil is in Fine-tuning and Long-tailed Problems:A New Benchmark for Scene Text Detection](https://mlsanity.com/paper/2505.15649/) (2025)
- [FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition](https://mlsanity.com/paper/2107.03166/) (2021)
- [SMILE: Sequence-to-Sequence Domain Adaption with Minimizing Latent Entropy for Text Image Recognition](https://mlsanity.com/paper/2202.11949/) (2022)
- [UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection](https://mlsanity.com/paper/2205.04683/) (2022)
- [Domain Adaptive Scene Text Detection via Subcategorization](https://mlsanity.com/paper/2212.00377/) (2022)

## Abstract (arXiv)

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.