  In many applications, such as autonomous driving, hand manipulation, or robot
navigation, object detection methods must be able to detect objects unseen in
the training set. Open World Detection(OWD) seeks to tackle this problem by
generalizing detection performance to seen and unseen class categories. Recent
works have seen success in the generation of class-agnostic proposals, which we
call Open-World Proposals(OWP), but this comes at the cost of a big drop on the
classification task when both tasks are considered in the detection model.
These works have investigated two-stage Region Proposal Networks (RPN) by
taking advantage of objectness scoring cues; however, for its simplicity,
run-time, and decoupling of localization and classification, we investigate OWP
through the lens of fully convolutional one-stage detection network, such as
FCOS. We show that our architectural and sampling optimizations on FCOS can
increase OWP performance by as much as 6% in recall on novel classes, marking
the first proposal-free one-stage detection network to achieve comparable
performance to RPN-based two-stage networks. Furthermore, we show that the
inherent, decoupled architecture of FCOS has benefits to retaining
classification performance. While two-stage methods worsen by 6% in recall on
novel classes, we show that FCOS only drops 2% when jointly optimizing for OWP
and classification.
