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On shopping websites, product images of low quality negatively affectcustomer experience. Although there are plenty of work in detecting images withdifferent defects, few efforts have been dedicated to correct those defects atscale. A major challenge is that there are thousands of product types and eachhas specific defects, therefore building defect specific models is unscalable.In this paper, we propose a unified Image-to-Image (I2I) translation model tocorrect multiple defects across different product types. Our model leverages anattention mechanism to hierarchically incorporate high-level defect groups andspecific defect types to guide the network to focus on defect-related imageregions. Evaluated on eight public datasets, our model reduces the FrechetInception Distance (FID) by 24.6% in average compared with MoNCE, thestate-of-the-art I2I method. Unlike public data, another practical challenge onshopping websites is that some paired images are of low quality. Therefore wedesign our model to be semi-paired by combining the L1 loss of paired data withthe cycle loss of unpaired data. Tested on a shopping website dataset tocorrect three image defects, our model reduces (FID) by 63.2% in averagecompared with WS-I2I, the state-of-the art semi-paired I2I method.

Hierarchical Conditional Semi-Paired Image-to-Image Translation For Multi-Task Image Defect Correction On Shopping Websites