Applications of Generative Adversarial Models in Visual Search Reformulation

TL;DR

This paper explores how generative adversarial models can be used to semantically transform visual queries, improving visual search by enabling query reformulation in fashion and product search contexts.

Contribution

It introduces methods for transforming visual queries using GAN latent space operations, addressing the challenge of query reformulation in visual search.

Findings

Demonstrates semantic transformation of visual queries using GANs

Improves relevance in fashion and product search results

Provides a framework for visual query reformulation

Abstract

Query reformulation is the process by which a input search query is refined by the user to match documents outside the original top-n results. On average, roughly 50% of text search queries involve some form of reformulation, and term suggestion tools are used 35% of the time when offered to users. As prevalent as text search queries are, however, such a feature has yet to be explored at scale for visual search. This is because reformulation for images presents a novel challenge to seamlessly transform visual features to match user intent within the context of a typical user session. In this paper, we present methods of semantically transforming visual queries, such as utilizing operations in the latent space of a generative adversarial model for the scenarios of fashion and product search.