Hierarchical Conditional Semi-Paired Image-to-Image Translation For
  Multi-Task Image Defect Correction On Shopping Websites
Moyan Li Jinmiao Fu Shaoyuan Xu Huidong Liu Jia Liu Bryan Wang
On shopping websites, product images of low quality negatively affect
customer experience. Although there are plenty of work in detecting images with
different defects, few efforts have been dedicated to correct those defects at
scale. A major challenge is that there are thousands of product types and each
has specific defects, therefore building defect specific models is unscalable.
In this paper, we propose a unified Image-to-Image (I2I) translation model to
correct multiple defects across different product types. Our model leverages an
attention mechanism to hierarchically incorporate high-level defect groups and
specific defect types to guide the network to focus on defect-related image
regions. Evaluated on eight public datasets, our model reduces the Frechet
Inception Distance (FID) by 24.6% in average compared with MoNCE, the
state-of-the-art I2I method. Unlike public data, another practical challenge on
shopping websites is that some paired images are of low quality. Therefore we
design our model to be semi-paired by combining the L1 loss of paired data with
the cycle loss of unpaired data. Tested on a shopping website dataset to
correct three image defects, our model reduces (FID) by 63.2% in average
compared with WS-I2I, the state-of-the art semi-paired I2I method.