  Segmenting primary objects in a video is an important yet challenging problem
in computer vision, as it exhibits various levels of foreground/background
ambiguities. To reduce such ambiguities, we propose a novel formulation via
exploiting foreground and background context as well as their complementary
constraint. Under this formulation, a unified objective function is further
defined to encode each cue. For implementation, we design a Complementary
Segmentation Network (CSNet) with two separate branches, which can
simultaneously encode the foreground and background information along with
joint spatial constraints. The CSNet is trained on massive images with manually
annotated salient objects in an end-to-end manner. By applying CSNet on each
video frame, the spatial foreground and background maps can be initialized. To
enforce temporal consistency effectively and efficiently, we divide each frame
into superpixels and construct neighborhood reversible flow that reflects the
most reliable temporal correspondences between superpixels in far-away frames.
With such flow, the initialized foregroundness and backgroundness can be
propagated along the temporal dimension so that primary video objects gradually
pop-out and distractors are well suppressed. Extensive experimental results on
three video datasets show that the proposed approach achieves impressive
performance in comparisons with 18 state-of-the-art models.

Computer Vision and Pattern Recognition