Studio2shop: From Studio Photo Shoots To Fashion Articles

Abstract

Fashion is an increasingly important topic in computer vision, in particular the so-called street-to-shop task of matching street images with shop images containing similar fashion items. Solving this problem promises new means of making fashion searchable and helping shoppers find the articles they are looking for. This paper focuses on finding pieces of clothing worn by a person in full-body or half-body images with neutral backgrounds. Such images are ubiquitous on the web and in fashion blogs, and are typically studio photos, we refer to this setting as studio-to-shop. Recent advances in computational fashion include the development of domain-specific numerical representations. Our model Studio2Shop builds on top of such representations and uses a deep convolutional network trained to match a query image to the numerical feature vectors of all the articles annotated in this image. Top-\(k\) retrieval evaluation on test query images shows that the correct items are most often found

Stats

• citations9
• S2 citations—
• github stars0
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• heat score7.50
• arxiv keylasserre2018studio2shop

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