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Open AccessArticle

Multimodal Sequential Fashion Attribute Prediction

NLP Group, Institute of Computer Science, University of Tartu, 50090 Tartu, Estonia
iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, Estonia
Rakuten Fits.Me, 50090 Tartu, Estonia
Faculty of Engineering, Hasan Kalyoncu University, Gaziantep 27900, Turkey
Author to whom correspondence should be addressed.
Information 2019, 10(10), 308;
Received: 15 July 2019 / Revised: 7 September 2019 / Accepted: 29 September 2019 / Published: 3 October 2019
(This article belongs to the Section Information Applications)
We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, the sequential model is also better able to generate sequences of attribute chains not seen during training. We also measure the contributions of both image and textual input and show that while text-only models always outperform image-only models, only the multimodal sequential model combining both image and text improves over the text-only model on all experimental datasets. View Full-Text
Keywords: Fashion E-Commerce; product attribute prediction; multimodal classification; sequential prediction; CNN; RNN Fashion E-Commerce; product attribute prediction; multimodal classification; sequential prediction; CNN; RNN
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Arslan, H.S.; Sirts, K.; Fishel, M.; Anbarjafari, G. Multimodal Sequential Fashion Attribute Prediction. Information 2019, 10, 308.

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