SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
This work addresses the problem of detecting object symmetry in real-world images for computer vision applications, but it is incremental as it builds on existing deep learning techniques with a new benchmark and method.
The paper tackles object symmetry detection in complex backgrounds by introducing a new benchmark, Sym-PASCAL, and an end-to-end deep learning method called Side-output Residual Network (SRN), which achieves state-of-the-art performance.
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.