Robust Training of Social Media Image Classification Models
<p>Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content are typically high, real-time image classification has become an urgent need for faster disaster response. Recent...
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2022
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| _version_ | 1864513562726105088 |
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| author | Firoj Alam (14158866) |
| author2 | Tanvirul Alam (14150628) Ferda Ofli (8983517) Muhammad Imran (282621) |
| author2_role | author author author |
| author_facet | Firoj Alam (14158866) Tanvirul Alam (14150628) Ferda Ofli (8983517) Muhammad Imran (282621) |
| author_role | author |
| dc.creator.none.fl_str_mv | Firoj Alam (14158866) Tanvirul Alam (14150628) Ferda Ofli (8983517) Muhammad Imran (282621) |
| dc.date.none.fl_str_mv | 2022-12-26T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tcss.2022.3230839 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Robust_Training_of_Social_Media_Image_Classification_Models/24056223 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation Data management and data science Machine learning Task analysis Social networking (online) Data models Training Benchmark testing Real-time systems Pipelines Crisis informatics Disaster response Humanitarian tasks Multitask learning Social media image classification |
| dc.title.none.fl_str_mv | Robust Training of Social Media Image Classification Models |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content are typically high, real-time image classification has become an urgent need for faster disaster response. Recent advances in computer vision and deep neural networks have enabled the development of models for image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of the damage. To develop robust models, it is necessary to understand the capability of the publicly available pretrained models for these tasks, which remains to be underexplored in the crisis informatics literature. In this study, we address such limitations by investigating ten different network architectures for four different tasks using the largest publicly available datasets for these tasks. We also explore various data augmentation strategies, semisupervised techniques, and a multitask learning setup. In our extensive experiments, we achieve promising results.</p><h2>Other Information</h2><p>Published in: IEEE Transactions on Computational Social Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tcss.2022.3230839" target="_blank">https://dx.doi.org/10.1109/tcss.2022.3230839</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_b87d57deba056d769ef6b98d40935d53 |
| identifier_str_mv | 10.1109/tcss.2022.3230839 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056223 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Robust Training of Social Media Image Classification ModelsFiroj Alam (14158866)Tanvirul Alam (14150628)Ferda Ofli (8983517)Muhammad Imran (282621)Information and computing sciencesComputer vision and multimedia computationData management and data scienceMachine learningTask analysisSocial networking (online)Data modelsTrainingBenchmark testingReal-time systemsPipelinesCrisis informaticsDisaster responseHumanitarian tasksMultitask learningSocial media image classification<p>Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content are typically high, real-time image classification has become an urgent need for faster disaster response. Recent advances in computer vision and deep neural networks have enabled the development of models for image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of the damage. To develop robust models, it is necessary to understand the capability of the publicly available pretrained models for these tasks, which remains to be underexplored in the crisis informatics literature. In this study, we address such limitations by investigating ten different network architectures for four different tasks using the largest publicly available datasets for these tasks. We also explore various data augmentation strategies, semisupervised techniques, and a multitask learning setup. In our extensive experiments, we achieve promising results.</p><h2>Other Information</h2><p>Published in: IEEE Transactions on Computational Social Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tcss.2022.3230839" target="_blank">https://dx.doi.org/10.1109/tcss.2022.3230839</a></p>2022-12-26T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tcss.2022.3230839https://figshare.com/articles/journal_contribution/Robust_Training_of_Social_Media_Image_Classification_Models/24056223CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240562232022-12-26T00:00:00Z |
| spellingShingle | Robust Training of Social Media Image Classification Models Firoj Alam (14158866) Information and computing sciences Computer vision and multimedia computation Data management and data science Machine learning Task analysis Social networking (online) Data models Training Benchmark testing Real-time systems Pipelines Crisis informatics Disaster response Humanitarian tasks Multitask learning Social media image classification |
| status_str | publishedVersion |
| title | Robust Training of Social Media Image Classification Models |
| title_full | Robust Training of Social Media Image Classification Models |
| title_fullStr | Robust Training of Social Media Image Classification Models |
| title_full_unstemmed | Robust Training of Social Media Image Classification Models |
| title_short | Robust Training of Social Media Image Classification Models |
| title_sort | Robust Training of Social Media Image Classification Models |
| topic | Information and computing sciences Computer vision and multimedia computation Data management and data science Machine learning Task analysis Social networking (online) Data models Training Benchmark testing Real-time systems Pipelines Crisis informatics Disaster response Humanitarian tasks Multitask learning Social media image classification |