  Video object segmentation (VOS) aims at pixel-level object tracking given
only the annotations in the first frame. Due to the large visual variations of
objects in video and the lack of training samples, it remains a difficult task
despite the upsurging development of deep learning. Toward solving the VOS
problem, we bring in several new insights by the proposed unified framework
consisting of object proposal, tracking and segmentation components. The object
proposal network transfers objectness information as generic knowledge into
VOS; the tracking network identifies the target object from the proposals; and
the segmentation network is performed based on the tracking results with a
novel dynamic-reference based model adaptation scheme. Extensive experiments
have been conducted on the DAVIS'17 dataset and the YouTube-VOS dataset, our
method achieves the state-of-the-art performance on several video object
segmentation benchmarks. We make the code publicly available at
https://github.com/sydney0zq/PTSNet.
