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Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter | | Zhenyu Wu
; Shuai Li
; Chenglizhao Chen
; Aimin Hao
; Hong Qin
; | | Date: |
10 Aug 2020 | | Abstract: | The real human attention is an interactive activity between our visual system
and our brain, using both low-level visual stimulus and high-level semantic
information. Previous image salient object detection (SOD) works conduct their
saliency predictions in a multi-task manner, i.e., performing pixel-wise
saliency 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 which
are semantically salient even in the case that these regions are perceptually
not the most salient ones at first glance. In this paper, we divide the SOD
problem into two sequential tasks: 1) we propose a lightweight, weakly
supervised deep network to coarsely locate those semantically salient regions
first; 2) then, as a post-processing procedure, we selectively fuse multiple
off-the-shelf deep models on these semantically salient regions as the
pixel-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-level
semantic ranks between multiple images", of which the methodology is more
consistent with the real human attention mechanism. Our method is simple yet
effective, which is the first attempt to consider the salient object detection
mainly as an object-level semantic re-ranking problem. | | Source: | arXiv, 2008.05397 | | Services: | Forum | Review | PDF | Favorites | |
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