  Open-world object detection (OWOD) is a challenging computer vision problem,
where the task is to detect a known set of object categories while
simultaneously identifying unknown objects. Additionally, the model must
incrementally learn new classes that become known in the next training
episodes. Distinct from standard object detection, the OWOD setting poses
significant challenges for generating quality candidate proposals on
potentially unknown objects, separating the unknown objects from the background
and detecting diverse unknown objects. Here, we introduce a novel end-to-end
transformer-based framework, OW-DETR, for open-world object detection. The
proposed OW-DETR comprises three dedicated components namely, attention-driven
pseudo-labeling, novelty classification and objectness scoring to explicitly
address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes
multi-scale contextual information, possesses less inductive bias, enables
knowledge transfer from known classes to the unknown class and can better
discriminate between unknown objects and background. Comprehensive experiments
are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive
ablations reveal the merits of our proposed contributions. Further, our model
outperforms the recently introduced OWOD approach, ORE, with absolute gains
ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of
incremental object detection, OW-DETR outperforms the state-of-the-art for all
settings on PASCAL VOC. Our code is available at
https://github.com/akshitac8/OW-DETR.
