Open World Object Detection (OWOD) combines open-set object detection with
incremental learning capabilities to handle the challenge of the open and
dynamic visual world. Existing works assume that a foreground predictor trained
on the seen categories can be directly transferred to identify the unseen
categories' locations by selecting the top-k most confident foreground
predictions. However, the assumption is hardly valid in practice. This is
because the predictor is inevitably biased to the known categories, and fails
under the shift in the appearance of the unseen categories. In this work, we
aim to build an unbiased foreground predictor by re-formulating the task under
Unsupervised Domain Adaptation, where the current biased predictor helps form
the domains: the seen object locations and confident background locations as
the source domain, and the rest ambiguous ones as the target domain. Then, we
adopt the simple and effective self-training method to learn a predictor based
on the domain-invariant foreground features, hence achieving unbiased
prediction robust to the shift in appearance between the seen and unseen
categories. Our approach's pipeline can adapt to various detection frameworks
and UDA methods, empirically validated by OWOD evaluation, where we achieve
state-of-the-art performance.

本研究旨在通过重新定义任务的方式，在无监督域适应的条件下构建一个无偏的前景预测器，利用领域内不变的前景特征学习预测器，从而实现对外观变化的应对能力，本方法在各种检测框架和无监督域适应方法中可实现自适应，经 OWOD 评估验证其在性能上达到了最新水平。