SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

Wei Ke, Jie Chen, Jianbin Jiao, Guoying Zhao, Qixiang Ye; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1068-1076

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Ke_2017_CVPR,
author = {Ke, Wei and Chen, Jie and Jiao, Jianbin and Zhao, Guoying and Ye, Qixiang},
title = {SRN: Side-output Residual Network for Object Symmetry Detection in the Wild},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}