  Open World Object Detection (OWOD) is a challenging computer vision problem
that requires detecting unknown objects and gradually learning the identified
unknown classes. However, it cannot distinguish unknown instances as multiple
unknown classes. In this work, we propose a novel OWOD problem called
Unknown-Classified Open World Object Detection (UC-OWOD). UC-OWOD aims to
detect unknown instances and classify them into different unknown classes.
Besides, we formulate the problem and devise a two-stage object detector to
solve UC-OWOD. First, unknown label-aware proposal and unknown-discriminative
classification head are used to detect known and unknown objects. Then,
similarity-based unknown classification and unknown clustering refinement
modules are constructed to distinguish multiple unknown classes. Moreover, two
novel evaluation protocols are designed to evaluate unknown-class detection.
Abundant experiments and visualizations prove the effectiveness of the proposed
method. Code is available at https://github.com/JohnWuzh/UC-OWOD.
