  This paper introduces an innovative approach to open world recognition (OWR),
where we leverage knowledge acquired from known objects to address the
recognition of previously unseen objects. The traditional method of object
modeling relies on supervised learning with strict closed-set assumptions,
presupposing that objects encountered during inference are already known at the
training phase. However, this assumption proves inadequate for real-world
scenarios due to the impracticality of accounting for the immense diversity of
objects. Our hypothesis posits that object appearances can be represented as
collections of "shareable" mid-level features, arranged in constellations to
form object instances. By adopting this framework, we can efficiently dissect
and represent both known and unknown objects in terms of their appearance cues.
Our paper introduces a straightforward yet elegant method for modeling novel or
unseen objects, utilizing established appearance cues and accounting for
inherent uncertainties. This representation not only enables the detection of
out-of-distribution objects or novel categories among unseen objects but also
facilitates a deeper level of reasoning, empowering the identification of the
superclass to which an unknown instance belongs. This novel approach holds
promise for advancing open world recognition in diverse applications.
