An object detector's ability to detect and flag \textit{novel} objects during
open-world deployments is critical for many real-world applications.
Unfortunately, much of the work in open object detection today is disjointed
and fails to adequately address applications that prioritize unknown object
recall \textit{in addition to} known-class accuracy. To close this gap, we
present a new task called Open-Set Object Detection and Discovery (OSODD) and
as a solution propose the Open-Set Regions with ViT features (OSR-ViT)
detection framework. OSR-ViT combines a class-agnostic proposal network with a
powerful ViT-based classifier. Its modular design simplifies optimization and
allows users to easily swap proposal solutions and feature extractors to best
suit their application. Using our multifaceted evaluation protocol, we show
that OSR-ViT obtains performance levels that far exceed state-of-the-art
supervised methods. Our method also excels in low-data settings, outperforming
supervised baselines using a fraction of the training data.

对于开放世界的部署，目标检测器检测和标记新对象的能力对很多实际应用非常关键。本研究提出了一种名为 Open-Set Object Detection and Discovery (OSODD) 的新任务，并且提出了一种称为 Open-Set Regions with ViT features (OSR-ViT) 的检测框架作为解决方案，该框架将无类别假设的倡议网络与强大的 ViT-based 分类器相结合。我们的评估结果表明，OSR-ViT 在性能上远超过现有的有监督方法，并且在低数据场景中表现出色，即使使用少量的训练数据也能优于有监督基线模型。