  We present an approach to semi-supervised video object segmentation, in the
context of the DAVIS 2017 challenge. Our approach combines category-based
object detection, category-independent object appearance segmentation and
temporal object tracking. We are motivated by the fact that the objects
semantic category tends not to change throughout the video while its appearance
and location can vary considerably. In order to capture the specific object
appearance independent of its category, for each video we train a fully
convolutional network using augmentations of the given annotated frame. We
refine the appearance segmentation mask with the bounding boxes provided either
by a semantic object detection network, when applicable, or by a previous frame
prediction. By introducing a temporal continuity constraint on the detected
boxes, we are able to improve the object segmentation mask of the appearance
network and achieve competitive results on the DAVIS datasets.
