The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …

Estimation of the human pose from a monocular camera has been an emerging research
topic in the computer vision community with many applications. Recently, benefiting from the …

Accurate medical image segmentation is essential for diagnosis and treatment planning of
diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art …

The field of explainable artificial intelligence (XAI) aims to uncover the inner workings of
complex deep neural models. While being crucial for safety-critical domains, XAI inherently …

Despite the great progress of person re-identification (ReID) with the adoption of
Convolutional Neural Networks, current ReID models are opaque and only outputs a scalar …

Deep neural networks (DNNs) have found their way into many applications with potential
impact on the safety, security, and fairness of human-machine-systems. Such require basic …

Recently, methods based on convolutional neural networks have achieved good results in
the dense prediction of remote-sensing images, particularly when employing normalized …