  Domain shift has always been one of the primary issues in video object
segmentation (VOS), for which models suffer from degeneration when tested on
unfamiliar datasets. Recently, many online methods have emerged to narrow the
performance gap between training data (source domain) and test data (target
domain) by fine-tuning on annotations of test data which are usually in
shortage. In this paper, we propose a novel method to tackle domain shift by
first introducing adversarial domain adaptation to the VOS task, with
supervised training on the source domain and unsupervised training on the
target domain. By fusing appearance and motion features with a convolution
layer, and by adding supervision onto the motion branch, our model achieves
state-of-the-art performance on DAVIS2016 with 82.6% mean IoU score after
supervised training. Meanwhile, our adversarial domain adaptation strategy
significantly raises the performance of the trained model when applied on
FBMS59 and Youtube-Object, without exploiting extra annotations.
