Image-based Recommendations on Styles and Substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, Anton van den Hengel

TL;DR
This paper presents a scalable, appearance-based model for understanding and recommending visual relationships between objects, such as clothing items, using large datasets and network inference techniques.
Contribution
It introduces a large-scale dataset and a novel network inference method to model human-like visual relationships without relying on detailed annotations.
Findings
Effective in recommending compatible clothing and accessories
Capable of distinguishing complementary and substitute relationships
Scalable approach suitable for large datasets
Abstract
Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Generative Adversarial Networks and Image Synthesis · Visual Attention and Saliency Detection
