Salient Object Detection (SOD) is a fundamental problem in the field of computer vision. This paper presents a novel Spatial Context-Aware Network (SCA-Net) for SOD in images. Compared with other recent deep learning based SOD algorithms, SCA-Net can more effectively aggregate multi-level deep features. A Long-Path Context Module (LPCM) is employed to grant better discrimination ability to feature maps that incorporate coarse global information. Consequently, a more accurate initial saliency map can be obtained to facilitate subsequent predictions. SCA-Net also adopts a Short-Path Context Module (SPCM) to progressively enforce the interaction between local contextual cues and global features. Extensive experiments on five large-scale benchmarks demonstrate that SCA-Net achieves favorable performance against very recent state-of-the-art algorithms.

显着物体检测（SOD）是计算机视觉领域中的一个基本问题。本文提出了一种用于图像中SOD的新型空间上下文感知网络（SCA-Net）。与最近的其他基于深度学习的SOD算法相比，SCA-Net可以更有效地聚合多层深度功能。长路径上下文模块（LPCM）用于为包含粗略全局信息的特征图赋予更好的辨别能力。因此，可以获得更准确的初始显着性图，以利于后续预测。SCA-Net还采用了短路径上下文模块（SPCM），以逐步加强本地上下文线索与全局功能之间的交互。在五个大型基准测试中进行的大量实验表明，SCA-Net与最新的最新算法相比，具有出色的性能。