  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 models in a computer vision context by 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.
