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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.ORCID iD: 0000-0002-4722-0823
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2019 (English)In: Proceedings - 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 1144-1151Conference paper, Published paper (Refereed)
Abstract [en]

Instagram as an online photo-sharing and socialnetworking 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 multilabeled classification compared to the other model.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 1144-1151
Keywords [en]
Dynamic CNN, Dynamic Computation Graph, Fashion Recommendation, Image Classification, Neural Pruning, Text Mining, Classification (of information), Cloud computing, Data mining, Large dataset, Text processing, Dynamic computations, Multiple attributes, Online Photo Sharing, Research challenges, Visual classification, Ubiquitous computing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-252212DOI: 10.1109/BDCloud.2018.00169Scopus ID: 2-s2.0-85063866491ISBN: 9781728111414 (print)OAI: oai:DiVA.org:kth-252212DiVA, id: diva2:1323014
Conference
16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, 11 December 2018 through 13 December 2018
Note

QC 20190611

Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-11

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Jaradat, ShathaDokoohaki, NimaHammar, KimMatskin, MihhailMatskin, Mihhail

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