13 citations · 26 across the 4 of their papers we have counts for
5 papers
Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections
Armin Kirchknopf, Djordje Slijepcevic, Ilkay Wunderlich +3
We investigate the problem of explainability for visual object detectors. Specifically, we demonstrate on the example of the YOLO object detector how to integrate Grad-CAM into the…
Explaining machine learning models for age classification in human gait analysis
Djordje Slijepcevic, Fabian Horst, Marvin Simak +7
Machine learning (ML) models have proven effective in classifying gait analysis data, e.g., binary classification of young vs. older adults. ML models, however, lack in providing h…
Explaining automated gender classification of human gait
Fabian Horst, Djordje Slijepcevic, Matthias Zeppelzauer +6
State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions. This "blac…
Multimodal Detection of Information Disorder from Social Media
Armin Kirchknopf, Djordje Slijepcevic, Matthias Zeppelzauer
Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the…
Bounded logit attention: Learning to explain image classifiers
Thomas Baumhauer, Djordje Slijepcevic, Matthias Zeppelzauer
Explainable artificial intelligence is the attempt to elucidate the workings of systems too complex to be directly accessible to human cognition through suitable side-information r…