Lecture By Dr.Chen jie of Shenzhen cyberspace laboratory
time: 2019-01-11

Speaker: Dr.Chen jie(Shenzhen cyberspace laboratory)

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

Time: Sun, Jan13, 2019, PM:15:30-16:30

Location: Room 4318, Building No.4, Wushan Campus


Abstract:

    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.