Hybrid Spiking Fully Convolutional Neural Network for Semantic Segmentation
Author:
Affiliation:
1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China
2. State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, Xidian University, Xi’an 710071, China
Abstract
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
The Fundamental Research Funds for the Central Universities
Publisher
MDPI AG
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Link
https://www.mdpi.com/2079-9292/12/17/3565/pdf
Reference38 articles.
1. Long, J., Shelhamer, E., and Darrell, T. (2015, January 7–12). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.
2. U-Net: Convolutional networks for biomedical image segmentation;Ronneberger;Med. Image Comput. Comput.-Assist. Interv.,2015
3. Chaurasia, A., and Culurciello, E. (2017, January 10–13). Linknet: Exploiting encoder representations for efficient semantic segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.
4. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs;Chen;IEEE Trans. Pattern Anal. Mach. Intell.,2017
5. Segnet: A deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell.,2017
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