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

arXiv: https://arxiv.org/abs/1703.04103 · HTML: https://mlsanity.com/paper/1703.04103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2017.

## Built on by (most cited first, 1 of 1)

- [Detecting Human Rights Violations on Social Media during Russia-Ukraine War](https://mlsanity.com/paper/2306.05370/) (2023)

## Abstract (arXiv)

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.