TITLE: MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification 

ABSTRACT: 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 (Available at: 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).
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 (Available at: 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).
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
http://arxiv.org/abs/2108.12828v4