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Conversational interfaces for the detail-oriented retail fashion domain aremore natural, expressive, and user friendly than classical keyword-based searchinterfaces. In this paper, we introduce the Fashion IQ dataset to support andadvance research on interactive fashion image retrieval. Fashion IQ is thefirst fashion dataset to provide human-generated captions that distinguishsimilar pairs of garment images together with side-information consisting ofreal-world product descriptions and derived visual attribute labels for theseimages. We provide a detailed analysis of the characteristics of the Fashion IQdata, and present a transformer-based user simulator and interactive imageretriever that can seamlessly integrate visual attributes with image features,user feedback, and dialog history, leading to improved performance over thestate of the art in dialog-based image retrieval. We believe that our datasetwill encourage further work on developing more natural and real-worldapplicable conversational shopping assistants.

Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback