  Open-World Object Detection (OWOD) extends object detection problem to a
realistic and dynamic scenario, where a detection model is required to be
capable of detecting both known and unknown objects and incrementally learning
newly introduced knowledge. Current OWOD models, such as ORE and OW-DETR, focus
on pseudo-labeling regions with high objectness scores as unknowns, whose
performance relies heavily on the supervision of known objects. While they can
detect the unknowns that exhibit similar features to the known objects, they
suffer from a severe label bias problem that they tend to detect all regions
(including unknown object regions) that are dissimilar to the known objects as
part of the background. To eliminate the label bias, this paper proposes a
novel approach that learns an unsupervised discriminative model to recognize
true unknown objects from raw pseudo labels generated by unsupervised region
proposal methods. The resulting model can be further refined by a
classification-free self-training method which iteratively extends pseudo
unknown objects to the unlabeled regions. Experimental results show that our
method 1) significantly outperforms the prior SOTA in detecting unknown objects
while maintaining competitive performance of detecting known object classes on
the MS COCO dataset, and 2) achieves better generalization ability on the LVIS
and Objects365 datasets.
