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Dynamic CNN Models For Fashion Recommendation in Instagram
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
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2018 (English)In: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, p. 1144-1151Conference paper, Published paper (Refereed)
Abstract [en]

Instagram as an online photo-sharing and social-networking service is becoming more powerful in enabling fashion brands to ramp up their business growth. Nowadays, a single post by a fashion influencer attracts a wealth of attention and a magnitude of followers who are curious to know more about the brands and style of each clothing item sitting inside the image. To this end, the development of efficient Deep CNN models that can accurately detect styles and brands have become a research challenge. In addition, current techniques need to cope with inherent fashion-related data issues. Namely, clothing details inside a single image only cover a small proportion of the large and hierarchical space of possible brands and clothing item attributes. In order to cope with these challenges, one can argue that neural classifiers should become adapted to large-scale and hierarchical fashion datasets. As a remedy, we propose two novel techniques to incorporate the valuable social media textual content to support the visual classification in a dynamic way. The first method is adaptive neural pruning (DynamicPruning) in which the clothing item category detected from posts' text analysis can be used to activate the possible range of connections of clothing attributes' classifier. The second method (DynamicLayers) is a dynamic framework in which multiple-attributes classification layers exist and a suitable attributes' classifier layer is activated dynamically based upon the mined text from the image. Extensive experiments on a dataset gathered from Instagram and a baseline fashion dataset (DeepFashion) have demonstrated that our approaches can improve the accuracy by about 20% when compared to base architectures. It is worth highlighting that with Dynamiclayers we have gained 35% accuracy for the task of multi-class multi-labeled classification compared to the other model.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2018. p. 1144-1151
Series
IEEE International Symposium on Parallel and Distributed Processing with Applications, ISSN 2158-9178
Keywords [en]
Neural Pruning, Dynamic Computation Graph, Dynamic CNN, Image Classification, Text Mining, Fashion Recommendation
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-252671DOI: 10.1109/BDCloud.2018.00169ISI: 000467843200155Scopus ID: 2-s2.0-85063866491ISBN: 978-1-7281-1141-4 (print)OAI: oai:DiVA.org:kth-252671DiVA, id: diva2:1319767
Conference
16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA
Note

QC 20190603

Available from: 2019-06-03 Created: 2019-06-03 Last updated: 2019-06-03Bibliographically approved

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Jaradat, ShathaDokoohaki, NimaHammar, KimMatskin, Mihhail

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