Deep Salient Object Detection With Dense Connections and Distraction Diagnosis

Abstract

In this paper, we propose two novel components for improving deep salient object detection models. The first component, called saliency detection network (S-Net), introduces dense short- and long-range connections that effectively integrate multiscale features to better exploit contexts at multiple levels. Benefiting from the direct access to low- and high-level features, the S-Net can not only exploit the object context but also preserve the object boundary sharply, leading to enhanced saliency detection performance. Second, a distraction detection network (D-Net) is developed to learn to diagnose which regions of an input image are distracting and harmful for saliency prediction of the S-Net. With such distraction diagnosis, the regions that are distracting to S-Net are removed in hindsight from the input image and the resulted distraction-free image is fed to S-Net for saliency prediction. To train the D-Net, a distraction mining approach is proposed to localize the model-specific distracting regions through examining the sensitiveness of the S-Net to image regions in a principled manner. Besides, the distraction mining approach also provides a way to interpret decisions made by deep neural network (DNN) saliency detection models, which relieves the black-box issues of DNNs to some extent. Extensive experiments on seven popular benchmark datasets demonstrate the effectiveness of the combined S-Net and D-Net, which provides new state of the arts.