MEDIC: a multi-task learning dataset for disaster image classification
<p>Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on explo...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513568007782400 |
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| author | Firoj Alam (14158866) |
| author2 | Tanvirul Alam (14150628) Md. Arid Hasan (14150631) Abul Hasnat (5561213) Muhammad Imran (282621) Ferda Ofli (8983517) |
| author2_role | author author author author author |
| author_facet | Firoj Alam (14158866) Tanvirul Alam (14150628) Md. Arid Hasan (14150631) Abul Hasnat (5561213) Muhammad Imran (282621) Ferda Ofli (8983517) |
| author_role | author |
| dc.creator.none.fl_str_mv | Firoj Alam (14158866) Tanvirul Alam (14150628) Md. Arid Hasan (14150631) Abul Hasnat (5561213) Muhammad Imran (282621) Ferda Ofli (8983517) |
| dc.date.none.fl_str_mv | 2022-09-03T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-022-07717-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/MEDIC_a_multi-task_learning_dataset_for_disaster_image_classification/21597078 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Earth sciences Physical geography and environmental geoscience Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Software engineering Multi-task learning Social media images Image classification Natural disasters Crisis informatics Deep learning Dataset |
| dc.title.none.fl_str_mv | MEDIC: a multi-task learning dataset for disaster image classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC (https://crisisnlp.qcri.org/medic/index.html), which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on multi-task learning, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. We experiment with different deep learning architectures and report promising results, which are above the majority baselines for all tasks. Along with the dataset, we also release all relevant scripts (https://github.com/firojalam/medic).</p><h2>Other Information</h2> <p> Published in: Neural Computing and Applications<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s00521-022-07717-0" target="_blank">http://dx.doi.org/10.1007/s00521-022-07717-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_19b7bb146134eec52aa1e0d69308f464 |
| identifier_str_mv | 10.1007/s00521-022-07717-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597078 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | MEDIC: a multi-task learning dataset for disaster image classificationFiroj Alam (14158866)Tanvirul Alam (14150628)Md. Arid Hasan (14150631)Abul Hasnat (5561213)Muhammad Imran (282621)Ferda Ofli (8983517)Earth sciencesPhysical geography and environmental geoscienceInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningSoftware engineeringMulti-task learningSocial media imagesImage classificationNatural disastersCrisis informaticsDeep learningDataset<p>Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC (https://crisisnlp.qcri.org/medic/index.html), which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on multi-task learning, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. We experiment with different deep learning architectures and report promising results, which are above the majority baselines for all tasks. Along with the dataset, we also release all relevant scripts (https://github.com/firojalam/medic).</p><h2>Other Information</h2> <p> Published in: Neural Computing and Applications<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s00521-022-07717-0" target="_blank">http://dx.doi.org/10.1007/s00521-022-07717-0</a></p>2022-09-03T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-022-07717-0https://figshare.com/articles/journal_contribution/MEDIC_a_multi-task_learning_dataset_for_disaster_image_classification/21597078CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215970782022-09-03T06:00:00Z |
| spellingShingle | MEDIC: a multi-task learning dataset for disaster image classification Firoj Alam (14158866) Earth sciences Physical geography and environmental geoscience Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Software engineering Multi-task learning Social media images Image classification Natural disasters Crisis informatics Deep learning Dataset |
| status_str | publishedVersion |
| title | MEDIC: a multi-task learning dataset for disaster image classification |
| title_full | MEDIC: a multi-task learning dataset for disaster image classification |
| title_fullStr | MEDIC: a multi-task learning dataset for disaster image classification |
| title_full_unstemmed | MEDIC: a multi-task learning dataset for disaster image classification |
| title_short | MEDIC: a multi-task learning dataset for disaster image classification |
| title_sort | MEDIC: a multi-task learning dataset for disaster image classification |
| topic | Earth sciences Physical geography and environmental geoscience Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Software engineering Multi-task learning Social media images Image classification Natural disasters Crisis informatics Deep learning Dataset |