  Focusing on only semantic instances that only salient in a scene gains more
benefits for robot navigation and self-driving cars than looking at all objects
in the whole scene. This paper pushes the envelope on salient regions in a
video to decompose them into semantically meaningful components, namely,
semantic salient instances. We provide the baseline for the new task of video
semantic salient instance segmentation (VSSIS), that is, Semantic Instance -
Salient Object (SISO) framework. The SISO framework is simple yet efficient,
leveraging advantages of two different segmentation tasks, i.e. semantic
instance segmentation and salient object segmentation to eventually fuse them
for the final result. In SISO, we introduce a sequential fusion by looking at
overlapping pixels between semantic instances and salient regions to have
non-overlapping instances one by one. We also introduce a recurrent instance
propagation to refine the shapes and semantic meanings of instances, and an
identity tracking to maintain both the identity and the semantic meaning of
instances over the entire video. Experimental results demonstrated the
effectiveness of our SISO baseline, which can handle occlusions in videos. In
addition, to tackle the task of VSSIS, we augment the DAVIS-2017 benchmark
dataset by assigning semantic ground-truth for salient instance labels,
obtaining SEmantic Salient Instance Video (SESIV) dataset. Our SESIV dataset
consists of 84 high-quality video sequences with pixel-wisely per-frame
ground-truth labels.
