Human Rights Violation Recognition

Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in images

We introduce the human rights archive (HRA) database, a verified-by-experts repository of 3050 human rights violations photographs, labeled with human rights semantic categories, comprising a list of the types of human rights abuses encountered at present. With the HRA dataset and a two-phase transfer learning scheme, we fine-tuned the state-of-the-art deep convolutional neural networks (CNNs) to provide human rights violations classification CNNs.

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

We conduct a rigorous evaluation on a common ground by combining the HRUN dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations.