  Object-based Novelty Detection (ND) aims to identify unknown objects that do
not belong to classes seen during training by an object detection model. The
task is particularly crucial in real-world applications, as it allows to avoid
potentially harmful behaviours, e.g. as in the case of object detection models
adopted in a self-driving car or in an autonomous robot. Traditional approaches
to ND focus on one time offline post processing of the pretrained object
detection output, leaving no possibility to improve the model robustness after
training and discarding the abundant amount of out-of-distribution data
encountered during deployment. In this work, we propose a novel framework for
object-based ND, assuming that human feedback can be requested on the predicted
output and later incorporated to refine the ND model without negatively
affecting the main object detection performance. This refinement operation is
repeated whenever new feedback is available. To tackle this new formulation of
the problem for object detection, we propose a lightweight ND module attached
on top of a pre-trained object detection model, which is incrementally updated
through a feedback loop. We also propose a new benchmark to evaluate methods on
this new setting and test extensively our ND approach against baselines,
showing increased robustness and a successful incorporation of the received
feedback.
