University of Oulu

W. Ke, J. Chen, J. Jiao, G. Zhao and Q. Ye, "SRN: Side-Output Residual Network for Object Symmetry Detection in the Wild," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 302-310. doi: 10.1109/CVPR.2017.40

SRN: side-output residual network for object symmetry detection in the wild

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Author: Ke, Wei1,2; Chen, Jie2; Jiao, Jianbin1;
Organizations: 1University of Chinese Academy of Sciences, Beijing, China
2CMVS, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2019-04-04


In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. The proposed symmetry detection approach, named Side-output Residual Network (SRN), leverages output Residual Units (RUs) to fit the errors between the object symmetry ground-truth and the outputs of RUs. By stacking RUs in a deep-to-shallow manner, SRN exploits the flow of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales. Experimental results validate both the benchmark and its challenging aspects related to real-world images, and the state-of-the-art performance of our symmetry detection approach. The benchmark and the code for SRN are publicly available at

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Series: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN: 1063-6919
ISSN-L: 1063-6919
ISBN: 978-1-5386-0457-1
ISBN Print: 978-1-5386-0458-8
Pages: 302 - 310
DOI: 10.1109/CVPR.2017.40
Host publication: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, Hawaii 21-26 July 2017
Conference: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This work is partially supported by NSFC under Grant 61671427, Beijing Municipal Science and Technology Commission under Grant Z161100001616005, and Science and Technology Innovation Foundation of Chinese Academy of Sciences under Grant CXJJ-16Q218. Tekes, Academy of Finland and Infotech Oulu are also gratefully acknowledged.
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