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.
This work tackles real-life traffic load recognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin it as MAT-CNN-SOPC, which uses an intelligent retraining mechanism of the CNN with known environments
We demonstrate that synthesized data achieve an improvement on mean average precision when used as training data and in conjunction with pre-trained CNN architectures
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.
We conduct a rigorous evaluation of how state-of-the art CNN architectures compare on a common ground over widely used material databases.
Automation of human rights violation recognition in images.
Recognizing visual material attributes in images.