论文标题
UC-OWOD：未知分类的开放世界对象检测
UC-OWOD: Unknown-Classified Open World Object Detection
论文作者
论文摘要
开放世界对象检测（OWOD）是一个具有挑战性的计算机视觉问题，需要检测未知的对象并逐渐学习已确定的未知类别。但是，它不能将未知实例区分为多个未知类别。在这项工作中，我们提出了一个新颖的OWOD问题，称为未知分类的开放世界对象检测（UC-OWOD）。 UC-OWOD旨在检测未知实例并将其分类为不同的未知类别。此外，我们提出问题，并设计一个两个阶段的对象检测器来解决UC-OWOD。首先，使用未知的标签意见建议和未知的歧视分类头用于检测已知和未知对象。然后，构建了基于相似性的未知分类和未知聚类改进模块，以区分多个未知类别。此外，设计了两个新的评估方案，以评估未知类别的检测。大量的实验和可视化证明了该方法的有效性。代码可在https://github.com/johnwuzh/uc-owod上找到。
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 https://github.com/JohnWuzh/UC-OWOD.
