DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dongmin | ko |
dc.contributor.author | Han, Sumin | ko |
dc.contributor.author | Son, Heesuk | ko |
dc.contributor.author | Lee, Dongman | ko |
dc.date.accessioned | 2020-11-12T23:55:23Z | - |
dc.date.available | 2020-11-12T23:55:23Z | - |
dc.date.created | 2020-11-08 | - |
dc.date.issued | 2020-05-12 | - |
dc.identifier.citation | 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, pp.869 - 880 | - |
dc.identifier.uri | http://hdl.handle.net/10203/277265 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | Springer | - |
dc.title | Human Activity Recognition Using Semi-supervised Multi-modal DEC for Instagram Data | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85085731651 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 869 | - |
dc.citation.endingpage | 880 | - |
dc.citation.publicationname | 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 | - |
dc.identifier.conferencecountry | SG | - |
dc.identifier.conferencelocation | Singapore | - |
dc.identifier.doi | 10.1007/978-3-030-47426-3_67 | - |
dc.contributor.localauthor | Lee, Dongman | - |
dc.contributor.nonIdAuthor | Kim, Dongmin | - |
dc.contributor.nonIdAuthor | Han, Sumin | - |
dc.contributor.nonIdAuthor | Son, Heesuk | - |
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