Convolutional neural network-based approaches have achieved remarkable
progress in semantic segmentation. However, these approaches heavily rely on
annotated data which are labor intensive. To cope with this limitation,
automatically annotated data generated from graphic engines are used to train
segmentation models. However, the models trained from synthetic data are
difficult to transfer to real images. To tackle this issue, previous works have
considered directly adapting models from the source data to the unlabeled
target data (to reduce the inter-domain gap). Nonetheless, these techniques do
not consider the large distribution gap among the target data itself
(intra-domain gap). In this work, we propose a two-step self-supervised domain
adaptation approach to minimize the inter-domain and intra-domain gap together.
First, we conduct the inter-domain adaptation of the model; from this
adaptation, we separate the target domain into an easy and hard split using an
entropy-based ranking function. Finally, to decrease the intra-domain gap, we
propose to employ a self-supervised adaptation technique from the easy to the
hard split. Experimental results on numerous benchmark datasets highlight the
effectiveness of our method against existing state-of-the-art approaches. The
source code is available at this https URL

本文提出了一个两步自监督域自适应方法，以最小化域间和域内差距，首先进行模型的域间自适应，然后使用基于熵的排序函数将目标域分为易和难两部分，最终采用自监督自适应技术从易到难来降低域内差距。