  Salient segmentation aims to segment out attention-grabbing regions, a
critical yet challenging task and the foundation of many high-level computer
vision applications. It requires semantic-aware grouping of pixels into salient
regions and benefits from the utilization of global multi-scale contexts to
achieve good local reasoning. Previous works often address it as two-class
segmentation problems utilizing complicated multi-step procedures including
refinement networks and complex graphical models. We argue that semantic
salient segmentation can instead be effectively resolved by reformulating it as
a simple yet intuitive pixel-pair based connectivity prediction task. Following
the intuition that salient objects can be naturally grouped via semantic-aware
connectivity between neighboring pixels, we propose a pure Connectivity Net
(ConnNet). ConnNet predicts connectivity probabilities of each pixel with its
neighboring pixels by leveraging multi-level cascade contexts embedded in the
image and long-range pixel relations. We investigate our approach on two tasks,
namely salient object segmentation and salient instance-level segmentation, and
illustrate that consistent improvements can be obtained by modeling these tasks
as connectivity instead of binary segmentation tasks for a variety of network
architectures. We achieve state-of-the-art performance, outperforming or being
comparable to existing approaches while reducing inference time due to our less
complex approach.
