Stacked U-Shape Network With Channel-Wise Attention for Salient Object Detection
Junxia Li, Zefeng Pan, Qingshan Liu, Ziyang Wang
Abstract
This paper addresses the core issue of how to learn powerful features for saliency. We have two major observations. First, feature maps of different layers in convolutional neural networks play different roles in saliency detection. Second, different feature channels in the same layer are not of equal importance to saliency, and they often have different response to foreground or background. To address these problems, a stacked U-shape network with channel-wise attention is presented to effectively utilize these features, which mainly consists of a parallel dilated convolution (PDC) module and a multi-level attention cascaded feedback (MACF) module. More specifically, PDC aims to enlarge the receptive field without increasing the computation and effectively avoid the gridding problem. MACF is innovatively designed to adaptively select the cross-layer complementary information, and the inter-dependencies between different channel maps in the same layer can be depicted well. Finally, we adopt a multi-layer loss function to improve the commonly used binary cross entropy loss which treats all pixels equally. The extensive experiments on five saliency detection datasets demonstrate that the proposed method outperforms the state-of-the-art approaches.