  Conversational interfaces for the detail-oriented retail fashion domain are
more natural, expressive, and user friendly than classical keyword-based search
interfaces. In this paper, we introduce the Fashion IQ dataset to support and
advance research on interactive fashion image retrieval. Fashion IQ is the
first fashion dataset to provide human-generated captions that distinguish
similar pairs of garment images together with side-information consisting of
real-world product descriptions and derived visual attribute labels for these
images. We provide a detailed analysis of the characteristics of the Fashion IQ
data, and present a transformer-based user simulator and interactive image
retriever that can seamlessly integrate visual attributes with image features,
user feedback, and dialog history, leading to improved performance over the
state of the art in dialog-based image retrieval. We believe that our dataset
will encourage further work on developing more natural and real-world
applicable conversational shopping assistants.
