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In this paper, we establish a baseline for object symmetry detection incomplex backgrounds by presenting a new benchmark and an end-to-end deeplearning approach, opening up a promising direction for symmetry detection inthe wild. The new benchmark, named Sym-PASCAL, spans challenges includingobject diversity, multi-objects, part-invisibility, and various complexbackgrounds that are far beyond those in existing datasets. The proposedsymmetry detection approach, named Side-output Residual Network (SRN),leverages output Residual Units (RUs) to fit the errors between the objectsymmetry groundtruth and the outputs of RUs. By stacking RUs in adeep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scalesto ease the problems of fitting complex outputs with limited layers,suppressing the complex backgrounds, and effectively matching object symmetryof different scales. Experimental results validate both the benchmark and itschallenging aspects related to realworld images, and the state-of-the-artperformance of our symmetry detection approach. The benchmark and the code forSRN are publicly available at https://github.com/KevinKecc/SRN.

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