  Open World Object Detection (OWOD) is a new and challenging computer vision
task that bridges the gap between classic object detection (OD) benchmarks and
object detection in the real world. In addition to detecting and classifying
seen/labeled objects, OWOD algorithms are expected to detect novel/unknown
objects - which can be classified and incrementally learned. In standard OD,
object proposals not overlapping with a labeled object are automatically
classified as background. Therefore, simply applying OD methods to OWOD fails
as unknown objects would be predicted as background. The challenge of detecting
unknown objects stems from the lack of supervision in distinguishing unknown
objects and background object proposals. Previous OWOD methods have attempted
to overcome this issue by generating supervision using pseudo-labeling -
however, unknown object detection has remained low. Probabilistic/generative
models may provide a solution for this challenge. Herein, we introduce a novel
probabilistic framework for objectness estimation, where we alternate between
probability distribution estimation and objectness likelihood maximization of
known objects in the embedded feature space - ultimately allowing us to
estimate the objectness probability of different proposals. The resulting
Probabilistic Objectness transformer-based open-world detector, PROB,
integrates our framework into traditional object detection models, adapting
them for the open-world setting. Comprehensive experiments on OWOD benchmarks
show that PROB outperforms all existing OWOD methods in both unknown object
detection ($\sim 2\times$ unknown recall) and known object detection ($\sim
10\%$ mAP). Our code will be made available upon publication at
https://github.com/orrzohar/PROB.
