Transferring scale-independent features to support multi-scale object recognition with deep convolutional neural network

被引：0
|
作者：
Zhou, Xiran [1 ]
机构：
[1] Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85287 USA
来源：
26TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2018) | 2018年
关键词：
Transfer learning; multi-scale object recognition; deep convolutional neural network; atrous region proposal;
D O I：
10.1145/3274895.3282797
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Objects are always represented by different scales on remote sensing imageries, which poses challenges for the state-of-the-art convolutional neural networks for multi-scale object recognition. This paper proposes atrous region proposal to facilitating detect other objects within different scales in an ad-hoc manner.
引用
收藏
页码：614 / 615
页数：2
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