  In this paper we illustrate how to perform both visual object tracking and
semi-supervised video object segmentation, in real-time, with a single simple
approach. Our method, dubbed SiamMask, improves the offline training procedure
of popular fully-convolutional Siamese approaches for object tracking by
augmenting their loss with a binary segmentation task. Once trained, SiamMask
solely relies on a single bounding box initialisation and operates online,
producing class-agnostic object segmentation masks and rotated bounding boxes
at 55 frames per second. Despite its simplicity, versatility and fast speed,
our strategy allows us to establish a new state of the art among real-time
trackers on VOT-2018, while at the same time demonstrating competitive
performance and the best speed for the semi-supervised video object
segmentation task on DAVIS-2016 and DAVIS-2017. The project website is
http://www.robots.ox.ac.uk/~qwang/SiamMask.
