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Image-to-Text Translation for Interactive Image Recognition: A Comparative User Study with Non-expert Users
https://ipsj.ixsq.nii.ac.jp/records/233729
https://ipsj.ixsq.nii.ac.jp/records/233729eb76318e-8ae5-4465-8628-61d0331f26f0
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-JNL6504010.pdf (8.1 MB)
2026年4月15日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥0, IPSJ:学会員：¥0, 論文誌:会員：¥0, DLIB:会員：¥0 | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2024-04-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Image-to-Text Translation for Interactive Image Recognition: A Comparative User Study with Non-expert Users | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Image-to-Text Translation for Interactive Image Recognition: A Comparative User Study with Non-expert Users | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [一般論文] interactive machine learning, graphical user interface | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| The University of Tokyo | ||||||||||
| 著者所属 | ||||||||||
| The University of Tokyo | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| The University of Tokyo | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| The University of Tokyo | ||||||||||
| 著者名 |
Wataru, Kawabe
× Wataru, Kawabe
× Yusuke, Sugano
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| 著者名(英) |
Wataru, Kawabe
× Wataru, Kawabe
× Yusuke, Sugano
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Interactive machine learning (IML) allows users to build their custom machine learning models without expert knowledge. While most existing IML systems are designed with classification algorithms, they sometimes oversimplify the capabilities of machine learning algorithms and restrict the user's task definition. On the other hand, as recent large-scale language models have shown, natural language representation has the potential to enable more flexible and generic task descriptions. Models that take images as input and output text have the potential to represent a variety of tasks by providing appropriate text labels for training. However, the effect of introducing text labels to IML system design has never been investigated. In this work, we aim to investigate the difference between image-to-text translation and image classification for IML systems. Using our prototype systems, we conducted a comparative user study with non-expert users, where participants solved various tasks. Our results demonstrate the underlying difficulty for users in properly defining image recognition tasks while highlighting the potential and challenges of interactive image-to-text translation systems. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.358 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Interactive machine learning (IML) allows users to build their custom machine learning models without expert knowledge. While most existing IML systems are designed with classification algorithms, they sometimes oversimplify the capabilities of machine learning algorithms and restrict the user's task definition. On the other hand, as recent large-scale language models have shown, natural language representation has the potential to enable more flexible and generic task descriptions. Models that take images as input and output text have the potential to represent a variety of tasks by providing appropriate text labels for training. However, the effect of introducing text labels to IML system design has never been investigated. In this work, we aim to investigate the difference between image-to-text translation and image classification for IML systems. Using our prototype systems, we conducted a comparative user study with non-expert users, where participants solved various tasks. Our results demonstrate the underlying difficulty for users in properly defining image recognition tasks while highlighting the potential and challenges of interactive image-to-text translation systems. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.358 ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 65, 号 4, 発行日 2024-04-15 |
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| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
| 公開者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
