Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows
Keisuke Shirai, Atsushi Hashimoto, Taichi Nishimura, Hirotaka Kameko, Shuhei Kurita, Yoshitaka Ushiku, Shinsuke Mori
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Abstract
We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn a cooking action result for each object in a recipe text. The dataset consists of object state changes and the workflow of the recipe text. The state change is represented as an image pair, while the workflow is represented as a recipe flow graph. We developed a web interface to reduce human annotation costs. The dataset allows us to try various applications, including multimodal information retrieval.- Anthology ID:
- 2022.coling-1.315
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3570–3577
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.315/
- DOI:
- Bibkey:
- Cite (ACL):
- Keisuke Shirai, Atsushi Hashimoto, Taichi Nishimura, Hirotaka Kameko, Shuhei Kurita, Yoshitaka Ushiku, and Shinsuke Mori. 2022. Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3570–3577, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows (Shirai et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.315.pdf
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@inproceedings{shirai-etal-2022-visual,
title = "Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows",
author = "Shirai, Keisuke and
Hashimoto, Atsushi and
Nishimura, Taichi and
Kameko, Hirotaka and
Kurita, Shuhei and
Ushiku, Yoshitaka and
Mori, Shinsuke",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.315/",
pages = "3570--3577",
abstract = "We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn a cooking action result for each object in a recipe text. The dataset consists of object state changes and the workflow of the recipe text. The state change is represented as an image pair, while the workflow is represented as a recipe flow graph. We developed a web interface to reduce human annotation costs. The dataset allows us to try various applications, including multimodal information retrieval."
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%0 Conference Proceedings %T Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows %A Shirai, Keisuke %A Hashimoto, Atsushi %A Nishimura, Taichi %A Kameko, Hirotaka %A Kurita, Shuhei %A Ushiku, Yoshitaka %A Mori, Shinsuke %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F shirai-etal-2022-visual %X We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn a cooking action result for each object in a recipe text. The dataset consists of object state changes and the workflow of the recipe text. The state change is represented as an image pair, while the workflow is represented as a recipe flow graph. We developed a web interface to reduce human annotation costs. The dataset allows us to try various applications, including multimodal information retrieval. %U https://aclanthology.org/2022.coling-1.315/ %P 3570-3577
Markdown (Informal)
[Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows](https://aclanthology.org/2022.coling-1.315/) (Shirai et al., COLING 2022)
- Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows (Shirai et al., COLING 2022)
ACL
- Keisuke Shirai, Atsushi Hashimoto, Taichi Nishimura, Hirotaka Kameko, Shuhei Kurita, Yoshitaka Ushiku, and Shinsuke Mori. 2022. Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3570–3577, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.