A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation

Junting Zhang; Chen Liang; C. -C. Jay Kuo

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arXiv · 1711.03694

A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation

Junting Zhang·Chen Liang·C. -C. Jay Kuo

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Abstract

A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain. The re-labeling and re-training processes alternate. With this design, the tri-branch network learns target-specific discriminative representations progressively and, as a result, the cross-domain capability of the segmenter improves. We evaluate the proposed network on large-scale domain adaptation experiments using both synthetic (GTA) and real (Cityscapes) images. It is shown that our solution achieves the state-of-the-art performance and it outperforms previous methods by a significant margin.

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BibTeX RIS

Junting Zhang, Chen Liang, C. -C. Jay Kuo. 2017-11-10. A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation. https://arxiv.org/abs/1711.03694

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