论文标题
纠正开放式对象检测：分类法，实际应用和适当的评估
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation
论文作者
论文摘要
开放式对象检测（OSOD）最近引起了人们的关注。它是在正确检测已知对象的同时检测未知对象。在本文中，我们首先指出，最近的研究对OSOD的形式化（概括开放式识别（OSR））有一个基本问题，因此概括了开放式识别（OSR），因此考虑了无限的各种未知对象。这个问题来自图像分类和对象检测之间的差异，因此很难正确评估OSOD方法的性能。然后，我们介绍了OSOD的新颖方案，该方案考虑了指定的超级类别类别中的已知和未知类别。这种新方案具有实用的应用程序，并且没有上述问题，可以正确评估OSOD性能，并可能使问题更易于管理。最后，我们使用多个数据集通过新方案对现有的OSOD方法进行了实验评估，这表明当前最新的OSOD方法获得了与简单基线方法相似的有限性能。本文还提出了OSOD的分类法，可以阐明不同的问题形式化。我们希望我们的研究有助于社区重新考虑OSOD问题并朝着正确的方向发展。
Open-set object detection (OSOD) has recently gained attention. It is to detect unknown objects while correctly detecting known objects. In this paper, we first point out that the recent studies' formalization of OSOD, which generalizes open-set recognition (OSR) and thus considers an unlimited variety of unknown objects, has a fundamental issue. This issue emerges from the difference between image classification and object detection, making it hard to evaluate OSOD methods' performance properly. We then introduce a novel scenario of OSOD, which considers known and unknown classes within a specified super-class of object classes. This new scenario has practical applications and is free from the above issue, enabling proper evaluation of OSOD performance and probably making the problem more manageable. Finally, we experimentally evaluate existing OSOD methods with the new scenario using multiple datasets, showing that the current state-of-the-art OSOD methods attain limited performance similar to a simple baseline method. The paper also presents a taxonomy of OSOD that clarifies different problem formalizations. We hope our study helps the community reconsider OSOD problems and progress in the right direction.
