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。

OSR-ViT：开放式目标检测与发现的简洁模块化框架

OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection  and Discovery

With the human pursuit of knowledge, open-set object detection (OSOD) has
been designed to identify unknown objects in a dynamic world. However, an issue
with the current setting is that all the predicted unknown objects share the
same category as "unknown", which require incremental learning via a
human-in-the-loop approach to label novel classes. In order to address this
problem, we present a new task, namely Open-Set Object Detection and Discovery
(OSODD). This new task aims to extend the ability of open-set object detectors
to further discover the categories of unknown objects based on their visual
appearance without human effort. We propose a two-stage method that first uses
an open-set object detector to predict both known and unknown objects. Then, we
study the representation of predicted objects in an unsupervised manner and
discover new categories from the set of unknown objects. With this method, a
detector is able to detect objects belonging to known classes and define novel
categories for objects of unknown classes with minimal supervision. We show the
performance of our model on the MS-COCO dataset under a thorough evaluation
protocol. We hope that our work will promote further research towards a more
robust real-world detection system.

本文提出了 OSODD 任务，使用开放式对象检测器识别未知对象的同时，基于其视觉外观无需人力，进一步发现未知对象的类别。我们提出了使用开放式对象检测器预测已知和未知对象的两阶段方法，然后以无监督的方式研究预测对象的表示，并从未知对象集合中发现新类别。在完整的评估协议下展示了我们模型在 MS-COCO 数据集上的性能。这项工作将促进更加强健的实际检测系统的研究。