  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.
