Dr Kyle Martin k.martin3@rgu.ac.uk
Lecturer
Human activity recognition with deep metric learners.
Martin, Kyle; Wijekoon, Anjana; Wiratunga, Nirmalie
Authors
Dr Anjana Wijekoon a.wijekoon1@rgu.ac.uk
Research Fellow B
Professor Nirmalie Wiratunga n.wiratunga@rgu.ac.uk
Associate Dean for Research
Contributors
Stelios Kapetanakis
Editor
Hayley Borck
Editor
Abstract
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.
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 the 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: http://ceur-ws.org/Vol-2567/paper1.pdf
Conference Name | 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19) |
---|---|
Conference Location | Otzenhausen, Germany |
Start Date | Sep 8, 2019 |
End Date | Sep 12, 2019 |
Acceptance Date | Aug 9, 2019 |
Online Publication Date | Jan 20, 2020 |
Publication Date | Mar 4, 2020 |
Deposit Date | Aug 15, 2019 |
Publicly Available Date | Mar 28, 2024 |
Publisher | CEUR Workshop Proceedings |
Pages | 8-17 |
Series Title | CEUR workshop proceedings |
Series Number | 2567 |
Series ISSN | 1613-0073 |
Keywords | Human activity recognition; Deep metric learning; Deep learning; Metric learning; Matching networks |
Public URL | https://rgu-repository.worktribe.com/output/350316 |
Publisher URL | http://ceur-ws.org/Vol-2567/paper1.pdf |
Files
MARTIN 2019 Human activity (v2)
(1.2 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
Evaluating a pass/fail grading model in first year undergraduate computing.
(2023)
Conference Proceeding
CBR driven interactive explainable AI.
(2023)
Conference Proceeding
iSee: intelligent sharing of explanation experience of users for users.
(2023)
Conference Proceeding
iSee: demonstration video. [video recording]
(2023)
Digital Artefact
Clinical dialogue transcription error correction using Seq2Seq models.
(2022)
Conference Proceeding
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search