Deep neural networks (DNNs) technique has achieved impressive performance on semantic segmentation, while its training process requires a large amount of pixel-wise labelled data. Domain adaptation, as a promising solution, can break the restriction by training the model on synthetic data, and generalising it in real-world data. However, there is still a lack of attention paid to the imbalance problems on semantic segmentation adaptation, including the imbalance problem between i) source and target data, ii) different classes. To solve these problems, a progressive hierarchical feature alignment method is proposed in this paper. To alleviate the data imbalance problem, the network is progressively trained by the data from multisource domains, so as to obtain domain-invariant features. To address the class imbalance problem, the features are aligned hierarchically across domains. According to the experimental results, our method shows the competitive adapted segmentation performance on three benchmark datasets.
Bibliographical noteThis work was supported by the University of Aberdeen Internal Funding to Pump-Prime Interdisciplinary Research and Impact under grant number SF10206-57
- Image segmentation
- convolution neural networks
- domain adaptation
- deep learning