论文标题
多步特征聚合框架，用于显着对象检测
Multistep feature aggregation framework for salient object detection
论文作者
论文摘要
关于显着对象检测的最新作品以一种方式利用了多尺度功能，以使高级功能和低级功能可以在定位显着对象方面进行协作。以前的许多方法在显着对象检测中都取得了出色的性能。通过合并高级和低级功能，可以提取大量功能信息。通常，他们在单向框架中进行这些操作，并将变量特征整合到最终功能输出。这可能会导致显着图的某些模糊或不准确的定位。为了克服这些困难，我们引入了一个多步出特征聚合（MSFA）框架以进行显着对象检测，该框架由三个模块组成，包括多样的接收（DR）模块，多尺度交互（MSI）模块和功能增强（FE）模块，以实现更好的多级特征融合。六个基准数据集的实验结果表明，MSFA实现了最先进的性能。
Recent works on salient object detection have made use of multi-scale features in a way such that high-level features and low-level features can collaborate in locating salient objects. Many of the previous methods have achieved great performance in salient object detection. By merging the high-level and low-level features, a large number of feature information can be extracted. Generally, they are doing these in a one-way framework, and interweaving the variable features all the way to the final feature output. Which may cause some blurring or inaccurate localization of saliency maps. To overcome these difficulties, we introduce a multistep feature aggregation (MSFA) framework for salient object detection, which is composed of three modules, including the Diverse Reception (DR) module, multiscale interaction (MSI) module and Feature Enhancement (FE) module to accomplish better multi-level feature fusion. Experimental results on six benchmark datasets demonstrate that MSFA achieves state-of-the-art performance.
