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
|Author:||Ke, Wei1,2; Chen, Jie2; Jiao, Jianbin1;|
1University of Chinese Academy of Sciences, Beijing, China
2CMVS, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019040411185
Institute of Electrical and Electronics Engineers,
|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 https://github.com/KevinKecc/SRN.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|Pages:||302 - 310|
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, Hawaii 21-26 July 2017
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
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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