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Understanding Fashion Trends from Street Photos via Neighbor-Constrained Embedding Learning
2017
Proceedings of the 2017 ACM on Multimedia Conference - MM '17
Driven by the increasing popular image-dominated social networks, such as Instagram, Pinterest and Chictopica, sharing of daily-life street photos now plays an influential role in fashion adoption between fashion trend-setters and followers. In this work, we propose a deep learning based fine-grained embedding learning approach for street fashion analysis by leveraging user-generated street fashion data. Specifically, we present QuadNet, an effective CNN based image embedding network driven by
doi:10.1145/3123266.3123441
dblp:conf/mm/GuWPS0K17
fatcat:qeqq77tuyvberp7iyuqvthrw5a