背景(考古学)
模式识别(心理学)
目标检测
突出
对象(语法)
特征(语言学)
空间关系
空间分析
计算机视觉
特征提取
卷积神经网络
作者
Yuqiu Kong,Mengyang Feng,Xin Li,Huchuan Lu,Xiuping Liu,Baocai Yin
标识
DOI:10.1016/j.patcog.2021.107867
摘要
Abstract 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.
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