Salient object detection has undergone a very rapid development with the blooming of Deep Neural Network (DNN), which is usually taken as an important preprocessing procedure in various computer vision tasks. However, the down-sampling operations, such as pooling and striding, always make the final predictions blurred at edges, which has seriously degenerated the performance of salient object detection. In this paper, we propose a simple yet effective approach, i.e. , Hierarchical and Interactive Refinement Network (HIRN), to preserve the edge structures in detecting salient objects. In particular, a novel multi-stage and dual-path network structure is designed to estimate the salient edges and regions from the low-level and high-level feature maps, respectively. As a result, the predicted regions will become more accurate by enhancing the weak responses at edges, while the predicted edges will become more semantic by suppressing the false positives in background. Once the salient maps of edges and regions are obtained at the output layers, a novel edge-guided inference algorithm is introduced to further filter the resulting regions along the predicted edges. Extensive experiments on several benchmark datasets have been conducted, in which the results show that our method significantly outperforms a variety of state-of-the-art approaches.

中文翻译：

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保留边缘显着对象检测的分层和交互式细化网络

随着深度神经网络（DNN）的兴起，显着物体检测得到了飞速发展，通常将其作为各种计算机视觉任务中的重要预处理程序。但是，下采样操作（例如合并和跨步）始终会使最终预测在边缘处变得模糊，这严重降低了显着目标检测的性能。在本文中，我们提出了一种简单而有效的方法，即 分层和交互式细化网络（HIRN），以保留检测突出物体时的边缘结构。特别是，设计了一种新颖的多级和双路径网络结构，以分别从低级和高级特征图中估计显着边缘和区域。结果，通过增强边缘处的弱响应，预测区域将变得更加准确，而通过抑制背景中的误报，预测边缘将变得更加语义化。一旦在输出层获得了边缘和区域的显着图，就会引入一种新颖的边缘引导推理算法，以沿着预测边缘进一步过滤所得区域。在几个基准数据集上进行了广泛的实验，