ProtoP-OD: Explainable Object Detection with Prototypical Parts
Pavlos Rath-Manakidis Frederik Strothmann Tobias Glasmachers Laurenz Wiskott
Interpretation and visualization of the behavior of detection transformers
tends to highlight the locations in the image that the model attends to, but it
provides limited insight into the \emph{semantics} that the model is focusing
on. This paper introduces an extension to detection transformers that
constructs prototypical local features and uses them in object detection. These
custom features, which we call prototypical parts, are designed to be mutually
exclusive and align with the classifications of the model. The proposed
extension consists of a bottleneck module, the prototype neck, that computes a
discretized representation of prototype activations and a new loss term that
matches prototypes to object classes. This setup leads to interpretable
representations in the prototype neck, allowing visual inspection of the image
content perceived by the model and a better understanding of the model's
reliability. We show experimentally that our method incurs only a limited
performance penalty, and we provide examples that demonstrate the quality of
the explanations provided by our method, which we argue outweighs the
performance penalty.