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The real human attention is an interactive activity between our visual systemand our brain, using both low-level visual stimulus and high-level semanticinformation. Previous image salient object detection (SOD) works conduct theirsaliency predictions in a multi-task manner, i.e., performing pixel-wisesaliency regression and segmentation-like saliency refinement at the same time,which degenerates their feature backbones in revealing semantic information.However, given an image, we tend to pay more attention to those regions whichare semantically salient even in the case that these regions are perceptuallynot the most salient ones at first glance. In this paper, we divide the SODproblem into two sequential tasks: 1) we propose a lightweight, weaklysupervised deep network to coarsely locate those semantically salient regionsfirst; 2) then, as a post-processing procedure, we selectively fuse multipleoff-the-shelf deep models on these semantically salient regions as thepixel-wise saliency refinement. In sharp contrast to the state-of-the-art(SOTA) methods that focus on learning pixel-wise saliency in "single image"using perceptual clues mainly, our method has investigated the "object-levelsemantic ranks between multiple images", of which the methodology is moreconsistent with the real human attention mechanism. Our method is simple yeteffective, which is the first attempt to consider the salient object detectionmainly as an object-level semantic re-ranking problem.

Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter