Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark
Shuhao Qiu
Chuang Zhu*
Wenli Zhou


In recent years, deep learning-based methods have shown promising results in computer vision area. However, a common deep learning model requires a large amount of labeled data, which is labor-intensive to collect and label. What’s more, the model can be ruined due to the domain shift between training data and testing data. Text recognition is a broadly studied field in computer vision and suffers from the same problems noted above due to the diversity of fonts and complicated backgrounds. In this paper, we focus on the text recognition problem and mainly make three contributions toward these problems. First, we collect a multi-source domain adaptation dataset for text recognition, including five different domains with over five million images, which is the first multi-domain text recognition dataset to our best knowledge. Secondly, we propose a new method called Meta Self-Learning, which combines the self-learning method with the meta-learning paradigm and achieves a better recognition result under the scene of multi domain adaptation. Thirdly, extensive experiments are conducted on the dataset to provide a benchmark and also show the effectiveness of our method.

Dataset Overview

Meta Self-training

The procedure of our method can be summarized as follow steps:

  1. The data from source domains with labels $D_S$ are used for warm-up;
  2. The model is evaluated on the target domain data without labels ${\overline{D}}_T$ and generates pseudo-labels;
  3. The target domain data with pseudo-labels $D_S$ and $\breve{D}_T$ are split randomly as ${\overline{M}}$ and $\widetilde{M}$;
  4. Meta train using ${\overline{M}}$;
  5. Meta test using $\widetilde{M}$;
  6. Outer optimization using a subset of $D_S$ and $\breve{D}_T$.

Video and PPT

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title={Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark},
author={Qiu, Shuhao and Zhu, Chuang and Zhou, Wenli},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},