  Class-Incremental learning (CIL) refers to the ability of artificial agents
to integrate new classes as they appear in a stream. It is particularly
interesting in evolving environments where agents have limited access to memory
and computational resources. The main challenge of incremental learning is
catastrophic forgetting, the inability of neural networks to retain past
knowledge when learning a new one. Unfortunately, most existing
class-incremental methods for object detection are applied to two-stage
algorithms such as Faster-RCNN, and rely on rehearsal memory to retain past
knowledge. We argue that those are not suitable in resource-limited
environments, and more effort should be dedicated to anchor-free and
rehearsal-free object detection. In this paper, we propose MultIOD, a
class-incremental object detector based on CenterNet. Our contributions are:
(1) we propose a multihead feature pyramid and multihead detection architecture
to efficiently separate class representations, (2) we employ transfer learning
between classes learned initially and those learned incrementally to tackle
catastrophic forgetting, and (3) we use a class-wise non-max-suppression as a
post-processing technique to remove redundant boxes. Results show that our
method outperforms state-of-the-art methods on two Pascal VOC datasets, while
only saving the model in its current state, contrary to other
distillation-based counterparts.
