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Human activity recognition with deep metric learners.

Martin, Kyle; Wijekoon, Anjana; Wiratunga, Nirmalie


Kyle Martin

Anjana Wijekoon


Stelios Kapetanakis

Hayley Borck


Establishing a strong foundation for similarity-based return is a top priority in Case-Based Reasoning (CBR) systems. Deep Metric Learners (DMLs) are a group of neural network architectures which learn to optimise case representations for similarity-based return by training upon multiple cases simultaneously to incorporate relationship knowledge. This is particularly important in the Human Activity Recognition (HAR) domain, where understanding similarity between cases supports aspects such as personalisation and open-ended HAR. In this paper, we perform a short review of three DMLs and compare their performance across three HAR datasets. Our findings support research which indicates DMLs are valuable to improve similarity-based return and indicate that considering more cases simultaneously offers better performance.

Start Date Sep 8, 2019
Publication Date Mar 4, 2020
Publisher CEUR Workshop Proceedings
Volume 2567
Pages 8-17
Series Title CEUR workshop proceedings
Series ISSN 1613-0073
Institution Citation MARTIN, K., WIJEKOON, A. and WIRATUNGA, N. 2019. Human activity recognition with deep metric learners. In Kapetanakis, S. and Borck, H. (eds.) Proceedings of 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19), 8-12 September 2019, Otzenhausen, Germany. CEUR Workshop Proceedings, 2567. Aachen: CEUR-WS [online], pages 8-17. Available from:
Keywords Human activity recognition; Deep metric learning; Deep learning; Metric learning; Matching networks
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