Incremental Object-Based Novelty Detection with Feedback Loop
Simone Caldarella Elisa Ricci Rahaf Aljundi
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.