  Current methods aggregate multi-level features or introduce edge and skeleton
to get more refined saliency maps. However, little attention is paid to how to
obtain the complete salient object in cluttered background, where the targets
are usually similar in color and texture to the background. To handle this
complex scene, we propose a sharp eyes network (SENet) that first seperates the
object from scene, and then finely segments it, which is in line with human
visual characteristics, i.e., to look first and then focus. Different from
previous methods which directly integrate edge or skeleton to supplement the
defects of objects, the proposed method aims to utilize the expanded objects to
guide the network obtain complete prediction. Specifically, SENet mainly
consists of target separation (TS) brach and object segmentation (OS) branch
trained by minimizing a new hierarchical difference aware (HDA) loss. In the TS
branch, we construct a fractal structure to produce saliency features with
expanded boundary via the supervision of expanded ground truth, which can
enlarge the detail difference between foreground and background. In the OS
branch, we first aggregate multi-level features to adaptively select
complementary components, and then feed the saliency features with expanded
boundary into aggregated features to guide the network obtain complete
prediction. Moreover, we propose the HDA loss to further improve the structural
integrity and local details of the salient objects, which assigns weight to
each pixel according to its distance from the boundary hierarchically. Hard
pixels with similar appearance in border region will be given more attention
hierarchically to emphasize their importance in completeness prediction.
Comprehensive experimental results on five datasets demonstrate that the
proposed approach outperforms the state-of-the-art methods both quantitatively
and qualitatively.
