  Incremental object detection (IOD) aims to train an object detector in
phases, each with annotations for new object categories. As other incremental
settings, IOD is subject to catastrophic forgetting, which is often addressed
by techniques such as knowledge distillation (KD) and exemplar replay (ER).
However, KD and ER do not work well if applied directly to state-of-the-art
transformer-based object detectors such as Deformable DETR and UP-DETR. In this
paper, we solve these issues by proposing a ContinuaL DEtection TRansformer
(CL-DETR), a new method for transformer-based IOD which enables effective usage
of KD and ER in this context. First, we introduce a Detector Knowledge
Distillation (DKD) loss, focusing on the most informative and reliable
predictions from old versions of the model, ignoring redundant background
predictions, and ensuring compatibility with the available ground-truth labels.
We also improve ER by proposing a calibration strategy to preserve the label
distribution of the training set, therefore better matching training and
testing statistics. We conduct extensive experiments on COCO 2017 and
demonstrate that CL-DETR achieves state-of-the-art results in the IOD setting.
