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Detection of Human Rights Violations in Images: Can Convolutional Neural Networks Help?

Kalliatakis, Grigorios and Ehsan, Shoaib and Fasli, Maria and Leonardis, Ales and Gall, Juergen and McDonald-Maier, Klaus D (2017) Detection of Human Rights Violations in Images: Can Convolutional Neural Networks Help? In: International Conference on Computer Vision Theory and Applications, 2017-02-27 - 2017-03-01, Porto, Portugal.

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Abstract

After setting the performance benchmarks for image, video, speech and audio processing, deep convolutional networks have been core to the greatest advances in image recognition tasks in recent times. This raises the question of whether there are any benefit in targeting these remarkable deep architectures with the unattempted task of recognising human rights violations through digital images. Under this perspective, we introduce a new, well-sampled human rights-centric dataset called Human Rights Understanding (HRUN). We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations. Experimental results on the HRUN dataset have shown that the best performing CNN architectures can achieve up to 88.10% mean average precision. Additionally, our experiments demonstrate that increasing the size of the training samples is crucial for achieving an improvement on mean average precision principally when utilising very deep networks.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 29 Apr 2020 21:15
Last Modified: 29 Apr 2020 21:15
URI: http://repository.essex.ac.uk/id/eprint/27399

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