Transfer Learning

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.

MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip

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

Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-world Training Data?

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

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.

Evaluating Deep Convolutional Neural Networks for Material Classification

We conduct a rigorous evaluation of how state-of-the art CNN architectures compare on a common ground over widely used material databases.

Visual Recognition of Human Rights Violations

Automation of human rights violation recognition in images.

Material Recognition

Recognizing visual material attributes in images.