Applications of Generative Adversarial Models in Visual Search Reformulation
This work addresses the problem of improving visual search usability for users in domains like e-commerce, but it is incremental as it adapts existing generative adversarial techniques to a new application area.
The paper tackles the challenge of visual search query reformulation, which lacks scalable solutions compared to text search, by proposing methods that use generative adversarial models to semantically transform visual queries, achieving a demonstration in fashion and product search contexts.
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.