  Because of its use in practice, open-world object detection (OWOD) has gotten
a lot of attention recently. The challenge is how can a model detect novel
classes and then incrementally learn them without forgetting previously known
classes. Previous approaches hinge on strongly-supervised or weakly-supervised
novel-class data for novel-class detection, which may not apply to real
applications. We construct a new benchmark that novel classes are only
encountered at the inference stage. And we propose a new OWOD detector YOLOOC,
based on the YOLO architecture yet for the Open-Class setup. We introduce label
smoothing to prevent the detector from over-confidently mapping novel classes
to known classes and to discover novel classes. Extensive experiments conducted
on our more realistic setup demonstrate the effectiveness of our method for
discovering novel classes in our new benchmark.
