论文标题
通过开放世界提案扩展一个阶段检测
Extending One-Stage Detection with Open-World Proposals
论文作者
论文摘要
在许多应用中，例如自动驾驶，手动操纵或机器人导航，对象检测方法必须能够检测训练集中看不见的对象。开放世界检测（OWD）试图通过将检测性能概括为可见和看不见的班级类别来解决这个问题。最近的作品在类不足的建议的一代中取得了成功，我们称之为开放世界的建议（OWP），但这是在检测模型中考虑了两个任务时，以分类任务的大量下降而付出了代价。这些作品通过利用目标评分线索来研究了两阶段的区域提案网络（RPN）。但是，为了简单，运行时间和分类和分类的解耦，我们通过完全卷积的一阶段检测网络（例如FCO）进行了OWP。我们表明，我们对FCO的架构和采样优化可以使OWP的性能在新颖类中提高6％，这标志着第一个无建议的一阶段检测网络，以实现与基于RPN的两阶段网络的可比性能。此外，我们表明，FCO的固有，脱钩的体系结构在保留分类性能方面具有好处。尽管在新颖的类别中，两阶段方法在召回率中却增加了6％，但我们表明，当共同优化OWP和分类时，FCO仅下降了2％。
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.
