  We propose a deep learning-based framework for instance-level object
segmentation. Our method mainly consists of three steps. First, We train a
generic model based on ResNet-101 for foreground/background segmentations.
Second, based on this generic model, we fine-tune it to learn instance-level
models and segment individual objects by using augmented object annotations in
first frames of test videos. To distinguish different instances in the same
video, we compute a pixel-level score map for each object from these
instance-level models. Each score map indicates the objectness likelihood and
is only computed within the foreground mask obtained in the first step. To
further refine this per frame score map, we learn a spatial propagation
network. This network aims to learn how to propagate a coarse segmentation mask
spatially based on the pairwise similarities in each frame. In addition, we
apply a filter on the refined score map that aims to recognize the best
connected region using spatial and temporal consistencies in the video.
Finally, we decide the instance-level object segmentation in each video by
comparing score maps of different instances.
