  Humans have a natural instinct to identify unknown object instances in their
environments. The intrinsic curiosity about these unknown instances aids in
learning about them, when the corresponding knowledge is eventually available.
This motivates us to propose a novel computer vision problem called: `Open
World Object Detection', where a model is tasked to: 1) identify objects that
have not been introduced to it as `unknown', without explicit supervision to do
so, and 2) incrementally learn these identified unknown categories without
forgetting previously learned classes, when the corresponding labels are
progressively received. We formulate the problem, introduce a strong evaluation
protocol and provide a novel solution, which we call ORE: Open World Object
Detector, based on contrastive clustering and energy based unknown
identification. Our experimental evaluation and ablation studies analyze the
efficacy of ORE in achieving Open World objectives. As an interesting
by-product, we find that identifying and characterizing unknown instances helps
to reduce confusion in an incremental object detection setting, where we
achieve state-of-the-art performance, with no extra methodological effort. We
hope that our work will attract further research into this newly identified,
yet crucial research direction.
