论文标题
班级：跨层次的关注和对显着物体检测的监督
CLASS: Cross-Level Attention and Supervision for Salient Objects Detection
论文作者
论文摘要
显着对象检测（SOD）是一项基本的计算机视觉任务。最近，随着深度神经网络的复兴，SOD取得了长足的进步。但是，仍然存在两个棘手的问题，这些问题无法通过现有方法，难以区分的区域和复杂的结构来很好地解决。为了解决这两个问题，在本文中，我们提出了一个新颖的深层网络，以供准确的SOD，名为Class。首先，为了利用低级和高级特征的不同优势，我们提出了一种新型的非本地跨层次注意（CLA），该注意可以捕获远程特征依赖关系，以增强完整明显对象的区别。其次，一种新颖的跨级监督（CLS）旨在通过像素级，区域级别和对象级学习复杂结构的互补上下文。那么，可以很好地恢复显着物体的精细结构和边界。在实验中，使用拟议的CLA和CL，我们的类网络。始终在五个数据集上胜过13种最先进的方法。
Salient object detection (SOD) is a fundamental computer vision task. Recently, with the revival of deep neural networks, SOD has made great progresses. However, there still exist two thorny issues that cannot be well addressed by existing methods, indistinguishable regions and complex structures. To address these two issues, in this paper we propose a novel deep network for accurate SOD, named CLASS. First, in order to leverage the different advantages of low-level and high-level features, we propose a novel non-local cross-level attention (CLA), which can capture the long-range feature dependencies to enhance the distinction of complete salient object. Second, a novel cross-level supervision (CLS) is designed to learn complementary context for complex structures through pixel-level, region-level and object-level. Then the fine structures and boundaries of salient objects can be well restored. In experiments, with the proposed CLA and CLS, our CLASS net. consistently outperforms 13 state-of-the-art methods on five datasets.
