About: Human Activity Recognition Using Semi-supervised Multi-modal DEC for Instagram Data

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Human Activity Recognition Using Semi-supervised Multi-modal DEC for Instagram Data
Creator	Han, Sumin Kim, Dongmin Lee, Dongman Son, Heesuk
source	PMC
abstract	Human Activity Recognition (HAR) using social media provides a solid basis for a variety of context-aware applications. Existing HAR approaches have adopted supervised machine learning algorithms using texts and their meta-data such as time, venue, and keywords. However, their recognition accuracy may decrease when applied to image-sharing social media where users mostly describe their daily activities and thoughts using both texts and images. In this paper, we propose a semi-supervised multi-modal deep embedding clustering method to recognize human activities on Instagram. Our proposed method learns multi-modal feature representations by alternating a supervised learning phase and an unsupervised learning phase. By utilizing a large number of unlabeled data, it learns a more generalized feature distribution for each HAR class and avoids overfitting to limited labeled data. Evaluation results show that leveraging multi-modality and unlabeled data is effective for HAR and our method outperforms existing approaches.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47426-3_67
has license	no-cc
sha1sum (hex)	2186bf359e5b204ba5595893f939645bccb97f6e
schema:url	https://doi.org/10.1007/978-3-030-47426-3_67
resource representing a document's title	Human Activity Recognition Using Semi-supervised Multi-modal DEC for Instagram Data
has PubMed Central identifier	PMC7206193
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:2186bf359e5b204ba5595893f939645bccb97f6e#body_text
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