  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 groundtruth 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 realworld 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.
