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MyEachtra: Event-Based Interactive Lifelog Retrieval System for LSC’23

Tran, Ly Duyen, Nguyen, Binh, Zhou, Liting and Gurrin, Cathal orcid logoORCID: 0000-0003-4395-7702 (2023) MyEachtra: Event-Based Interactive Lifelog Retrieval System for LSC’23. In: The 6th Annual ACM Lifelog Search Challenge. ISBN 979-8-4007-0188

Abstract
Retrieval is a fundamental challenge within the research community of lifelog and the Lifelog Search Challenge (LSC) has been an important annual benchmarking activity for interactive lifelog retrieval systems since 2018. This paper proposes MyEachtra (/maiAK-truh/), a system designed for the upcoming LSC’23 workshop. Improved upon MyScéal, which was the top performing system from LSC’20 to LSC’22, MyEachtra includes modifications to address the challenges of non-owner user understanding of lifelog contexts and open-ended lifelog question answering. Specifically, MyEachtra shifts the focus from images to events as retrieval units. Events are segmented using location metadata as well as visual and time differences between successive images. A pilot study on different approaches to aggregate images into events was conducted to test the automatic performance of the system, which showed promising results. For known-item queries, showing only the top 3 events proved to be adequate to find relevant images. However, future evaluation of the performance for ad-hoc and question-answering queries is necessary for a complete analysis of the MyEachtra.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:lifelog, interactive retrieval system, pretrained models, user modeling
Subjects:Computer Science > Interactive computer systems
Computer Science > Information storage and retrieval systems
Computer Science > Lifelog
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the 6th Annual ACM Lifelog Search Challenge. . Association for Computing Machinery New York, NY, United States. ISBN 979-8-4007-0188
Publisher:Association for Computing Machinery New York, NY, United States
Funders:18/CRT/6224
ID Code:29497
Deposited On:29 Apr 2024 14:02 by Ly Duyen Tran . Last Modified 29 Apr 2024 14:02
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