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.

本文研究在目标检测中如何应对训练集中未出现的物体，提出了一种称为 Open World Detection (OWD) 的解决方案，并探究了将 Open-World Proposals (OWP) 应用于全卷积单阶段检测网络的可能性和优化方式，实验结果表明该方法在处理未知类别时召回率可提高 6%，且相对于双阶段检测网络，该方法可更好地保持分类性能。