Model-agnostic explanation methods for deep learning models are flexible regarding usability and availability. However, due to the fact that they can only manipulate input to see changes in output, they suffer from weak performance when used with complex model architectures. For models with large inputs as, for instance, in object detection, sampling-based methods like KernelSHAP are inefficient due to many computation-heavy forward passes through the model. In this work, we present a framework for using sampling-based explanation methods in a computer vision context shown for body part relevance assessment for pedestrian detection. Furthermore, we introduce a novel sampling-based method similar to KernelSHAP that shows more robustness for lower sampling sizes and, thus, is more efficient for explainability analyses on large-scale datasets. We demonstrate our relevance assessment method on simulation data acquired with the CARLA simulator. In the end, our method enables the benchmarking and performance comparison of various pedestrian detection models based on human-interpretable semantic regions.

Thanks to the progress in precision agriculture and remote sensing, automatic visual disease severity scoring of largescale fields is getting more efficient. This potentially allows the investigation and observation of disease spread dynamics and infection incidence in real-world field trials. Furthermore, phytopathological research has been obtaining comprehensive knowledge about pathogens and epidemiology for decades. In this work, we want to leverage modern data-driven Machine Learning (ML) methods and connect them with available knowledge incorporated by simulation techniques. As a result, we show how to create infection datasets from disease severity annotations and environmental data that can serve as training data for a transformer-based ML model. First preliminary results show coherent, interpretable results that motivate for further optimization and improvements of our modeling strategy.