  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 https://github.com/feipan664/IntraDA.git.
