In-the-Wild Video Question Answering
Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, Rada Mihalcea
Abstract
Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the “in the wild” settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https: //lit.eecs.umich.edu/wildqa/.- Anthology ID:
- 2022.coling-1.496
- 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:
- 5613–5635
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.496
- DOI:
- Bibkey:
- Cite (ACL):
- Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, and Rada Mihalcea. 2022. In-the-Wild Video Question Answering. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5613–5635, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- In-the-Wild Video Question Answering (Castro et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.496.pdf
- Data
- MovieQA, TVQA, TVQA+
Export citation
@inproceedings{castro-etal-2022-wild,
title = "In-the-Wild Video Question Answering",
author = "Castro, Santiago and
Deng, Naihao and
Huang, Pingxuan and
Burzo, Mihai and
Mihalcea, Rada",
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-1.496",
pages = "5613--5635",
abstract = "Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the {``}in the wild{''} settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https: //lit.eecs.umich.edu/wildqa/.",
}
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%0 Conference Proceedings %T In-the-Wild Video Question Answering %A Castro, Santiago %A Deng, Naihao %A Huang, Pingxuan %A Burzo, Mihai %A Mihalcea, Rada %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 castro-etal-2022-wild %X Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the “in the wild” settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https: //lit.eecs.umich.edu/wildqa/. %U https://aclanthology.org/2022.coling-1.496 %P 5613-5635
Markdown (Informal)
[In-the-Wild Video Question Answering](https://aclanthology.org/2022.coling-1.496) (Castro et al., COLING 2022)
- In-the-Wild Video Question Answering (Castro et al., COLING 2022)
ACL
- Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, and Rada Mihalcea. 2022. In-the-Wild Video Question Answering. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5613–5635, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.