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.
@article{arxiv.1910.12460,
title = {Applications of Generative Adversarial Models in Visual Search Reformulation},
author = {Kyle Xiao and Houdong Hu and Yan Wang},
journal= {arXiv preprint arXiv:1910.12460},
year = {2019}
}