  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
https://github.com/GKalliatakis/DisplaceNet.
