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Research Article | Open Access

Learning adaptive receptive fields for deep image parsing networks

Zhen Wei1,2, Yao Sun1( ), Junyu Lin3, Si Liu1,4
State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
University of Chinese Academy of Sciences, Beijing 101408, China.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing 210094, China.
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Abstract

In this paper, we introduce a novel approach to automatically regulate receptive fields in deep image parsing networks. Unlike previous work which placed much importance on obtaining better receptive fields using manually selected dilated convolutional kernels, our approach uses two affine transformation layers in the network’s backbone and operates on feature maps. Feature maps are inflated or shrunk by the new layer, thereby changing the receptive fields in the following layers. By use of end-to-end training, the whole framework is data-driven, without laborious manual intervention. The proposed method is generic across datasets and different tasks. We have conducted extensive experiments on both general image parsing tasks, and face parsing tasks as concrete examples, to demonstrate the method’s superior ability to regulate over manual designs.

Keywords

semantic segmentationreceptive fielddata-drivenface parsing

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Computational Visual Media
Volume 4 Issue 3,
September 2018
Pages 231-244
DOI: 10.1007/s41095-018-0112-1
Cite this article:
Wei Z, Sun Y, Lin J, et al. Learning adaptive receptive fields for deep image parsing networks. Computational Visual Media, 2018, 4(3): 231-244. https://doi.org/10.1007/s41095-018-0112-1

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Revised: 06 December 2017
Accepted: 14 January 2018
Published: 04 April 2018
© The Author(s) 2018

This article is published with open access at Springerlink.com

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