Abstract In recent years, fully convolutional neural network (FCN) has broken all records in various vision task. It also achieves great performance in salient object detection. However, most of the state-of-the-art methods have suffered from the challenge of precisely segmenting the entire salient object with uniform region and explicit boundary and effectively suppressing the backgrounds on complex images. There is still a large room for improvement over the FCN-based saliency detection approaches. In this paper, we propose an attention and boundary guided deep neural network for salient object detection to better locate and segment the salient objects with uniform interior and explicit boundary. A channel-wise attention module is utilized to emphasize the important regions, which selects the important feature channels and assigns large weights to them. A boundary information localization module is proposed for suppressing the irrelevant boundary information to better locate and explore the useful structure of objects. The proposed approach achieves state-of-the-art performance on four well-known benchmark datasets.

中文翻译：

---

注意和边界引导的显着对象检测

摘要 近年来，全卷积神经网络（FCN）在各种视觉任务中打破了所有记录。它还在显着目标检测方面取得了出色的表现。然而，大多数最先进的方法都面临着精确分割具有均匀区域和明确边界的整个显着对象以及有效抑制复杂图像背景的挑战。与基于 FCN 的显着性检测方法相比，仍有很大的改进空间。在本文中，我们提出了一种用于显着对象检测的注意力和边界引导的深度神经网络，以更好地定位和分割具有统一内部和显式边界的显着对象。通道级注意力模块用于强调重要区域，它选择重要的特征通道并为其分配较大的权重。提出了一种边界信息定位模块，用于抑制不相关的边界信息，以更好地定位和探索对象的有用结构。所提出的方法在四个著名的基准数据集上实现了最先进的性能。