Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region ...
For glaciologists, studying ice sheets from the polar regions is critical. With the advancement of deep learning techniques, we can now extract high-level information from the ice sheet data (e.g., estimating the ice layer thickness, predicting the ice accumulation for upcoming years, etc.). However...
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2023
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Online Access: | https://dx.doi.org/10.13016/m25psb-lmjw https://mdsoar.org/handle/11603/27595 |
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ftdatacite:10.13016/m25psb-lmjw 2023-08-27T04:10:00+02:00 Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region ... Sarkar, Argho Rahnemoonfar, Maryam 2023 https://dx.doi.org/10.13016/m25psb-lmjw https://mdsoar.org/handle/11603/27595 unknown Maryland Shared Open Access Repository Creative Commons Attribution 4.0 International Attribution 4.0 International (CC BY 4.0) This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CreativeWork article 2023 ftdatacite https://doi.org/10.13016/m25psb-lmjw 2023-08-07T14:24:23Z For glaciologists, studying ice sheets from the polar regions is critical. With the advancement of deep learning techniques, we can now extract high-level information from the ice sheet data (e.g., estimating the ice layer thickness, predicting the ice accumulation for upcoming years, etc.). However, a vision-based conversational deep learning approach has not been explored yet, where scientists can get information by asking questions about images. In this paper, we have introduced the task of Visual Question Answering (VQA) on remote-sensed ice sheet imagery. To study, we have presented a unique VQA dataset, Polar-VQA, in this study. All the images in this dataset were collected using four types of airborne radars. The main objective of this research is to highlight the importance of VQA in the context of ice sheet research and conduct a baseline study of existing VQA approaches on Polar-VQA dataset. ... Article in Journal/Newspaper Ice Sheet DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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description |
For glaciologists, studying ice sheets from the polar regions is critical. With the advancement of deep learning techniques, we can now extract high-level information from the ice sheet data (e.g., estimating the ice layer thickness, predicting the ice accumulation for upcoming years, etc.). However, a vision-based conversational deep learning approach has not been explored yet, where scientists can get information by asking questions about images. In this paper, we have introduced the task of Visual Question Answering (VQA) on remote-sensed ice sheet imagery. To study, we have presented a unique VQA dataset, Polar-VQA, in this study. All the images in this dataset were collected using four types of airborne radars. The main objective of this research is to highlight the importance of VQA in the context of ice sheet research and conduct a baseline study of existing VQA approaches on Polar-VQA dataset. ... |
format |
Article in Journal/Newspaper |
author |
Sarkar, Argho Rahnemoonfar, Maryam |
spellingShingle |
Sarkar, Argho Rahnemoonfar, Maryam Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region ... |
author_facet |
Sarkar, Argho Rahnemoonfar, Maryam |
author_sort |
Sarkar, Argho |
title |
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region ... |
title_short |
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region ... |
title_full |
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region ... |
title_fullStr |
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region ... |
title_full_unstemmed |
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region ... |
title_sort |
polar-vqa: visual question answering on remote sensed ice sheet imagery from polar region ... |
publisher |
Maryland Shared Open Access Repository |
publishDate |
2023 |
url |
https://dx.doi.org/10.13016/m25psb-lmjw https://mdsoar.org/handle/11603/27595 |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_rights |
Creative Commons Attribution 4.0 International Attribution 4.0 International (CC BY 4.0) This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.13016/m25psb-lmjw |
_version_ |
1775351726483701760 |