SRN: Side-Output Residual Network for Object Reflection Symmetry Detection and Beyond

This article establishes a baseline for object reflection symmetry detection in natural images by releasing a new benchmark named Sym-PASCAL and proposing an end-to-end deep learning approach for reflection symmetry. Sym-PASCAL spans challenges of multiobjects, object diversity, part invisibility, and clustered backgrounds, which is far beyond those in existing data sets. The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual units (RUs) to fit the errors between the symmetry ground truth and the side outputs of multiple stages of a trunk network. By cascading RUs from deep to shallow, SRN exploits the flow of errors along multiple stages to effectively matching object symmetry at different scales and suppress the clustered backgrounds. SRN is interpreted as a boosting-like algorithm, which assembles features using RUs during network forward and backward propagations. SRN is further upgraded to a multitask SRN (MT-SRN) for joint symmetry and edge detection, demonstrating its generality to image-to-mask learning tasks. Experimental results verify that the Sym-PASCAL benchmark is challenging related to real-world images, SRN achieves state-of-the-art performance, and MT-SRN has the capability to simultaneously predict edge and symmetry mask without loss of performance.

Automated summary

This study introduces a new benchmark named Sym-PASCAL and proposes an end-to-end deep learning approach for object reflection symmetry detection in natural images. The method leverages a side-output residual network (SRN) to fit errors between symmetry ground truth and side outputs of multiple stages, achieving state-of-the-art performance in challenging real-world image tasks. The SRN is further enhanced to multitask SRN (MT-SRN) for joint symmetry and edge detection without performance loss.