Open World Object Detection (OWOD) is a challenging computer vision problem
that requires detecting unknown objects and gradually learning the identified
unknown classes. However, it cannot distinguish unknown instances as multiple
unknown classes. In this work, we propose a novel OWOD problem called
Unknown-Classified Open World Object Detection (UC-OWOD). UC-OWOD aims to
detect unknown instances and classify them into different unknown classes.
Besides, we formulate the problem and devise a two-stage object detector to
solve UC-OWOD. First, unknown label-aware proposal and unknown-discriminative
classification head are used to detect known and unknown objects. Then,
similarity-based unknown classification and unknown clustering refinement
modules are constructed to distinguish multiple unknown classes. Moreover, two
novel evaluation protocols are designed to evaluate unknown-class detection.
Abundant experiments and visualizations prove the effectiveness of the proposed
method. Code is available at this https URL

本文提出了一种新的 Open World Object Detection 问题，称为 Unknown-Classified Open World Object Detection (UC-OWOD)，并构建了两个阶段的目标检测器，以解决它所涉及的未知分类问题，并设计了两个新的评估协议用于评估未知类别检测的有效性。

UC-OWOD: 未知类别的开放世界目标检测

UC-OWOD: Unknown-Classified Open World Object Detection

Open set recognition is an emerging research area that aims to simultaneously
classify samples from predefined classes and identify the rest as 'unknown'. In
this process, one of the key challenges is to reduce the risk of generalizing
the inherent characteristics of numerous unknown samples learned from a small
amount of known data. In this paper, we propose a new concept, Reciprocal
Point, which is the potential representation of the extra-class space
corresponding to each known category. The sample can be classified to known or
unknown by the otherness with reciprocal points. To tackle the open set
problem, we offer a novel open space risk regularization term. Based on the
bounded space constructed by reciprocal points, the risk of unknown is reduced
through multi-category interaction. The novel learning framework called
Reciprocal Point Learning (RPL), which can indirectly introduce the unknown
information into the learner with only known classes, so as to learn more
compact and discriminative representations. Moreover, we further construct a
new large-scale challenging aircraft dataset for open set recognition: Aircraft
300 (Air-300). Extensive experiments on multiple benchmark datasets indicate
that our framework is significantly superior to other existing approaches and
achieves state-of-the-art performance on standard open set benchmarks.

该研究提出了新的概念 'Reciprocal Point'，并应用于开放集识别的新学习框架 'Reciprocal Point Learning' 中，用于降低多个未知样本对少量已知数据的风险，并通过边界空间分离未知和已知数据，进一步排除风险，最终在 'Aircraft 300' 等标准数据集上实现了最佳性能。