Abstract
Effective convolutional features play an important role
in saliency estimation but how to learn powerful features
for saliency is still a challenging task. FCN-based methods directly apply multi-level convolutional features without distinction, which leads to sub-optimal results due to
the distraction from redundant details. In this paper, we
propose a novel attention guided network which selectively
integrates multi-level contextual information in a progressive manner. Attentive features generated by our network
can alleviate distraction of background thus achieve better
performance. On the other hand, it is observed that most
of existing algorithms conduct salient object detection by
exploiting side-output features of the backbone feature extraction network. However, shallower layers of backbone
network lack the ability to obtain global semantic information, which limits the effective feature learning. To address
the problem, we introduce multi-path recurrent feedback to
enhance our proposed progressive attention driven framework. Through multi-path recurrent connections, global
semantic information from the top convolutional layer is
transferred to shallower layers, which intrinsically refines
the entire network. Experimental results on six benchmark
datasets demonstrate that our algorithm performs favorably
against the state-of-the-art approaches