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.
翻译:开放天体探测( OSOD) 近来引起注意 。 在正确检测已知天体的同时, 检测未知天体 。 在本文中, 我们首先指出, 最近的研究 将OSOD 正式化, 将开放天体识别( OSR) 常规化, 并因此考虑无限种类的未知天体, 具有根本性的问题 。 这个问题来自图像分类和天体探测之间的差异, 使得很难正确评估 OOD 方法的性能 。 然后我们引入了一种新的 OSOD 设想, 将已知的和未知的种类纳入指定的超级天体类别 。 这一新设想具有实用性, 并且不受上述问题的影响, 使得对 OODD 性能进行适当的评估, 并可能使问题更加容易处理 。 最后, 我们利用多个数据集实验性地评估现有的操作方法, 表明当前最先进的OSOD 方法的性能有限, 类似于简单的基线方法 。 我们的文件还展示了OSODD 的分类, 澄清了不同问题的正规化。 我们希望我们的研究能帮助社区重新考虑OSODD 问题和在正确方向上的进展 。