  Training models continually to detect and classify objects, from new classes
and new domains, remains an open problem. In this work, we conduct a thorough
analysis of why and how object detection models forget catastrophically. We
focus on distillation-based approaches in two-stage networks; the most-common
strategy employed in contemporary continual object detection work.Distillation
aims to transfer the knowledge of a model trained on previous tasks -- the
teacher -- to a new model -- the student -- while it learns the new task. We
show that this works well for the region proposal network, but that wrong, yet
overly confident teacher predictions prevent student models from effective
learning of the classification head. Our analysis provides a foundation that
allows us to propose improvements for existing techniques by detecting
incorrect teacher predictions, based on current ground-truth labels, and by
employing an adaptive Huber loss as opposed to the mean squared error for the
distillation loss in the classification heads. We evidence that our strategy
works not only in a class incremental setting, but also in domain incremental
settings, which constitute a realistic context, likely to be the setting of
representative real-world problems.
