  Despite the recent advances in the field of object detection, common
architectures are still ill-suited to incrementally detect new categories over
time. They are vulnerable to catastrophic forgetting: they forget what has been
already learned while updating their parameters in absence of the original
training data. Previous works extended standard classification methods in the
object detection task, mainly adopting the knowledge distillation framework.
However, we argue that object detection introduces an additional problem, which
has been overlooked. While objects belonging to new classes are learned thanks
to their annotations, if no supervision is provided for other objects that may
still be present in the input, the model learns to associate them to background
regions. We propose to handle these missing annotations by revisiting the
standard knowledge distillation framework. Our approach outperforms current
state-of-the-art methods in every setting of the Pascal-VOC dataset. We further
propose an extension to instance segmentation, outperforming the other
baselines. Code can be found here: https://github.com/fcdl94/MMA
