增采样
计算机科学
人工智能
突出
SPARK(编程语言)
棱锥(几何)
特征(语言学)
卷积(计算机科学)
目标检测
计算机视觉
特征提取
编码(集合论)
模式识别(心理学)
比例(比率)
对象(语法)
图像(数学)
人工神经网络
集合(抽象数据类型)
程序设计语言
哲学
物理
光学
语言学
量子力学
作者
Yu-Huan Wu,Yun Liu,Le Zhang,Ming–Ming Cheng,Bo Ren
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:31: 3125-3136
被引量:109
标识
DOI:10.1109/tip.2022.3164550
摘要
Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and discovering fine details, respectively. However, most efforts are devoted to low-level feature learning by fusing multi-scale features or enhancing boundary representations. High-level features, which although have long proven effective for many other tasks, yet have been barely studied for SOD. In this paper, we tap into this gap and show that enhancing high-level features is essential for SOD as well. To this end, we introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image, leading to accurate salient object localization. To accomplish better multi-level feature fusion, we construct the Scale-Correlated Pyramid Convolution (SCPC) to build an elegant decoder for recovering object details from the above extreme downsampling. Extensive experiments demonstrate that EDN achieves state-of-the-art performance with real-time speed. Our efficient EDN-Lite also achieves competitive performance with a speed of 316fps. Hence, this work is expected to spark some new thinking in SOD. Code is available at https://github.com/yuhuan-wu/EDN.
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