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DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level

Kalliatakis, Grigorios and Ehsan, Shoaib and Fasli, Maria and McDonald-Maier, Klaus (2019) DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level. Working Paper. arXiv. (Unpublished)


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Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at

Item Type: Monograph (Working Paper)
Additional Information: To be published in CVPR Workshop on Computer Vision for Global Challenges (CV4GC). arXiv admin note: substantial text overlap with arXiv:1902.03817
Uncontrolled Keywords: cs.CV
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 22 May 2020 15:52
Last Modified: 15 Jan 2022 01:28

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