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2019 | OriginalPaper | Buchkapitel

Deep Fashion Analysis with Feature Map Upsampling and Landmark-Driven Attention

verfasst von : Jingyuan Liu, Hong Lu

Erschienen in: Computer Vision – ECCV 2018 Workshops

Verlag: Springer International Publishing

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Abstract

In this paper, we propose an attentive fashion network to address three problems of fashion analysis, namely landmark localization, category classification and attribute prediction. By utilizing a landmark prediction branch with upsampling network structure, we boost the accuracy of fashion landmark localization. With the aid of the predicted landmarks, a landmark-driven attention mechanism is proposed to help improve the precision of fashion category classification and attribute prediction. Experimental results show that our approach outperforms the state-of-the-arts on the DeepFashion dataset.

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Vorheriges Kapitel Tiered Deep Similarity Search for Fashion

Nächstes Kapitel DesIGN: Design Inspiration from Generative Networks

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Titel: Deep Fashion Analysis with Feature Map Upsampling and Landmark-Driven Attention
verfasst von: Jingyuan Liu
Hong Lu
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018 Workshops
Print ISBN: 978-3-030-11014-7

Electronic ISBN: 978-3-030-11015-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-11015-4_4

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