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Visual search is of great assistance in reseller commerce, especially fornon-tech savvy users with affinity towards regional languages. It allowsresellers to accurately locate the products that they seek, unlike textualsearch which recommends products from head brands. Product attributes availablein e-commerce have a great potential for building better visual search systemsas they capture fine grained relations between data points. In this work, wedesign a visual search system for reseller commerce using a multi-task learningapproach. We also highlight and address the challenges like image compression,cropping, scribbling on the image, etc, faced in reseller commerce. Our modelconsists of three different tasks: attribute classification, triplet rankingand variational autoencoder (VAE). Masking technique is used for designing theattribute classification. Next, we introduce an offline triplet miningtechnique which utilizes information from multiple attributes to capturerelative order within the data. This technique displays a better performancecompared to the traditional triplet mining baseline, which uses singlelabel/attribute information. We also compare and report incremental gainachieved by our unified multi-task model over each individual task separately.The effectiveness of our method is demonstrated using the in-house dataset ofproduct images from the Lifestyle business-unit of Flipkart, India's largeste-commerce company. To efficiently retrieve the images in production, we usethe Approximate Nearest Neighbor (ANN) index. Finally, we highlight ourproduction environment constraints and present the design choices andexperiments conducted to select a suitable ANN index.

Visually Similar Products Retrieval for Shopsy