Tran, Quang-Linh ORCID: 0000-0002-5409-0916, Nguyen, Binh, Jones, Gareth J. F.
ORCID: 0000-0003-2923-8365 and Gurrin, Cathal
ORCID: 0000-0003-2903-3968
(2024)
MemoriQA: A Question-Answering Lifelog Dataset.
In: 1st ACM Workshop on AI-Powered Q&A Systems for Multimedia.
ISBN 9798400705472
Abstract
Lifelogging can be referred to as the process of passively collecting data on an individual's daily life. Lifelog data provides a large amount of information which can be used to understand the lifelogger's lifestyle and preferences. This data can also support the lifeloggers in saving their memories and important moments. Question-answering (QA) is a common task in natural language processing (NLP) and can be extended to multi-modal such as the visual question-answering task. QA for lifelog data can be described as the task of answering questions about a lifelogger's past using lifelog data, which can significantly help lifeloggers understand their life by asking questions about their lifelog. QA for lifelogs can also provide useful insights into lifelogger's life for those exploring their lifelog. This paper presents the MemoriQA lifelog dataset designed to explore the question-answering task for lifelogs. This dataset provides 61-day lifelog images and other lifelog data such as internet activity, health metrics, music listening history and GPS. A comprehensive annotation process is performed to create the description as well as question-answer pairs. We propose some methods to address the QA in lifelog problem in this paper.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Workshop |
Refereed: | Yes |
Uncontrolled Keywords: | Lifelog Dataset, Personal Lifelog Archive, Question Answering |
Subjects: | Computer Science > Lifelog |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Institutes and Centres > ADAPT |
Published in: | Proceedings of the 1st ACM Workshop on AI-Powered Q&A Systems for Multimedia. AIQAM '24 . Association for Computing Machinery. ISBN 9798400705472 |
Publisher: | Association for Computing Machinery |
Official URL: | https://dl.acm.org/doi/10.1145/3643479.3662050 |
Copyright Information: | Authors |
Funders: | ADAPT Centre |
ID Code: | 30198 |
Deposited On: | 13 Aug 2024 11:22 by Linh Tran . Last Modified 13 Aug 2024 11:22 |
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