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Retrieving clothes which are worn in social media videos (Instagram, TikTok)is the latest frontier of e-fashion, referred to as "video-to-shop" in thecomputer vision literature. In this paper we present MovingFashion, the firstpublicly available dataset to cope with this challenge. MovingFashion iscomposed of 14855 social videos, each one of them associated to e-commerce"shop" images where the corresponding clothing items are clearly portrayed. Inaddition, we present a network for retrieving the shop images in this scenario,dubbed SEAM Match-RCNN. The model is trained by image-to-video domainadaptation, allowing to use video sequences where only their association with ashop image is given, eliminating the need of millions of annotated boundingboxes. SEAM Match-RCNN builds an embedding, where an attention-based weightedsum of few frames (10) of a social video is enough to individuate the correctproduct within the first 5 retrieved items in a 14K+ shop element gallery withan accuracy of 80%. This provides the best performance on MovingFashion,comparing exhaustively against the related state-of-the-art approaches andalternative baselines.

MovingFashion: a Benchmark for the Video-to-Shop Challenge