Class-Incremental learning (CIL) is the ability of artificial agents to
accommodate 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 class-incremental learning
is catastrophic forgetting, the inability of neural networks to retain past
knowledge when learning a new one. Unfortunately, most existing
class-incremental object detectors are applied to two-stage algorithms such as
Faster-RCNN and rely on rehearsal memory to retain past knowledge. We believe
that the current benchmarks are not realistic, and more effort should be
dedicated to anchor-free and rehearsal-free object detection. In this context,
we propose MultIOD, a class-incremental object detector based on CenterNet. Our
main 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. Without bells and whistles, our method outperforms a range of
state-of-the-art methods on two Pascal VOC datasets.

基于 CenterNet 的 MultIOD 是一种无需复习和复述的类增量目标检测器，通过多头特征金字塔和多头检测架构高效分离类别表示，利用迁移学习和类别非极大值抑制技术解决灾难性遗忘问题，并在两个 Pascal VOC 数据集上显著优于一系列最先进的方法。