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.

翻译：开放世界目标检测将目标检测问题扩展至一个现实且动态的场景，要求检测模型既能检测已知和未知物体，又能增量学习新引入的知识。当前开放世界目标检测模型（如ORE和OW-DETR）侧重于将具有高目标性得分的区域伪标注为未知物体，其性能严重依赖于已知物体的监督。这些模型虽能检测出与已知物体特征相似的未知物体，但存在严重的标签偏差问题——倾向于将所有与已知物体不相似的区域（包括未知物体区域）检测为背景。为消除标签偏差，本文提出一种新方法，通过学习无监督判别模型，从无监督区域提议方法生成的原始伪标签中识别真实未知物体。通过无分类的自训练方法迭代地将伪未知物体扩展至未标注区域，可进一步优化所得模型。实验结果表明，本方法：1）在MS COCO数据集上，检测未知物体的性能显著优于现有最先进方法，同时保持已知物体类别的竞争力；2）在LVIS和Objects365数据集上展现出更好的泛化能力。