Due to the rapid development of deep learning, the performance of salient object detection has been constantly refreshed. Nevertheless, it is still challenging for existing methods to distinguish the location of salient objects and retain fine structural details. In this paper, a novel progressive dual-attention residual network (PDRNet) is proposed to exploit two complementary attention maps to guide residual learning, thus progressively refining prediction in a coarse-to-fine manner. We design a dual-attention residual module (DRM) to achieve residual refinement with the help of the dual attention (DA) scheme. Specifically, an attention map and its corresponding reverse attention map are used to make the network be aware of learning residual details from the perspective of the salient and non-salient regions, thus utilizing their complementarity to correct the mistakes of object parts and boundary details. Besides, a hierarchical feature screening module (HFSM) is designed to capture more powerful global contextual knowledge for locating salient objects. It establishes cross-scale skip connections among multi-scale features and utilizes the intra-channel dependency of these scales to enhance information interaction and feature representation. Extensive experiments have proved that our proposed PDRNet performs favorably against 18 state-of-the-art competitors on five benchmark datasets, demonstrating the effectiveness and superiority of our method.

中文翻译：

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用于显着目标检测的渐进式双注意力残差网络


由于深度学习的快速发展，显着目标检测的性能不断刷新。然而，现有方法区分显着物体的位置并保留精细的结构细节仍然具有挑战性。在本文中，提出了一种新颖的渐进双注意力残差网络（PDRNet），利用两个互补的注意力图来指导残差学习，从而以从粗到细的方式逐步细化预测。我们设计了一个双注意力残差模块（DRM），以借助双注意力（DA）方案实现残差细化。具体来说，使用注意力图及其相应的反向注意力图来使网络意识到从显着区域和非显着区域的角度学习残留细节，从而利用它们的互补性来纠正对象部分和边界细节的错误。此外，分层特征筛选模块（HFSM）旨在捕获更强大的全局上下文知识来定位显着对象。它在多尺度特征之间建立跨尺度跳跃连接，并利用这些尺度的通道内依赖性来增强信息交互和特征表示。大量实验证明，我们提出的 PDRNet 在 5 个基准数据集上与 18 个最先进的竞争对手相比表现出色，证明了我们方法的有效性和优越性。