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

页码：614 / 615

页数：2

共 49 条

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