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.

通过使用不同类型的深度卷积神经网络结构来识别人权侵犯，我们在 Human Rights Understanding 数据集上进行了实验，结果显示最好表现的 CNN 结构可以获得高达 88.10％的平均精度，并且当使用非常深的网络时，增加训练样本量对于获得改进至关重要。