  Open world object detection aims at detecting objects that are absent in the
object classes of the training data as unknown objects without explicit
supervision. Furthermore, the exact classes of the unknown objects must be
identified without catastrophic forgetting of the previous known classes when
the corresponding annotations of unknown objects are given incrementally. In
this paper, we propose a two-stage training approach named Open World DETR for
open world object detection based on Deformable DETR. In the first stage, we
pre-train a model on the current annotated data to detect objects from the
current known classes, and concurrently train an additional binary classifier
to classify predictions into foreground or background classes. This helps the
model to build an unbiased feature representations that can facilitate the
detection of unknown classes in subsequent process. In the second stage, we
fine-tune the class-specific components of the model with a multi-view
self-labeling strategy and a consistency constraint. Furthermore, we alleviate
catastrophic forgetting when the annotations of the unknown classes becomes
available incrementally by using knowledge distillation and exemplar replay.
Experimental results on PASCAL VOC and MS-COCO show that our proposed method
outperforms other state-of-the-art open world object detection methods by a
large margin.
