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TALS: A framework for text analysis, fine-grained annotation, localisation and semantic segmentation
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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2019 (English)In: Proceedings - International Computer Software and Applications Conference, IEEE Computer Society, 2019, Vol. 8754470, p. 201-206Conference paper, Published paper (Refereed)
Abstract [en]

With around 2.77 billion users using online social media platforms nowadays, it is becoming more attractive for business retailers to reach and to connect to more potential clients through social media. However, providing more effective recommendations to grab clients’ attention requires a deep understanding of users’ interests. Given the enormous amounts of text and images that users share in social media, deep learning approaches play a major role in performing semantic analysis of text and images. Moreover, object localisation and pixel-by-pixel semantic segmentation image analysis neural architectures provide an enhanced level of information. However, to train such architectures in an end-to-end manner, detailed datasets with specific meta-data are required. In our paper, we present a complete framework that can be used to tag images in a hierarchical fashion, and to perform object localisation and semantic segmentation. In addition to this, we show the value of using neural word embeddings in providing additional semantic details to annotators to guide them in annotating images in the system. Our framework is designed to be a fully functional solution capable of providing fine-grained annotations, essential localisation and segmentation services while keeping the core architecture simple and extensible. We also provide a fine-grained labelled fashion dataset that can be a rich source for research purposes.

Place, publisher, year, edition, pages
IEEE Computer Society, 2019. Vol. 8754470, p. 201-206
Series
Proceedings - International Computer Software and Applications Conference, ISSN 0730-3157
Keywords [en]
Annotations, Dataset, Deep learning, Fine-grained, Localisation, Natural language processing, Semantic segmentation, Word embeddings
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262582DOI: 10.1109/COMPSAC.2019.10207Scopus ID: 2-s2.0-85072655269ISBN: 9781728126074 (print)OAI: oai:DiVA.org:kth-262582DiVA, id: diva2:1363188
Conference
43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019; Milwaukee; United States; 15 July 2019 through 19 July 2019
Note

QC 20191022

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22Bibliographically approved

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Jaradat, ShathaDokoohaki, NimaWara, UmmalHammar, KimMatskin, Mihhail

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