We tackle the challenging problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images. The main difficulty arises from the absence of supervision for these unknown classes, making it challenging to distinguish them from the background. Existing OSOD detectors either fail to properly exploit or inadequately leverage the abundant unlabeled unknown objects in training data, restricting their performance. To address these limitations, we propose UADet, an Uncertainty-Aware Open-Set Object Detector that considers appearance and geometric uncertainty. By integrating these uncertainty measures, UADet effectively reduces the number of unannotated instances incorrectly utilized or omitted by previous methods. Extensive experiments on OSOD benchmarks demonstrate that UADet substantially outperforms previous state-of-the-art (SOTA) methods in detecting both known and unknown objects, achieving a 1.8x improvement in unknown recall while maintaining high performance on known classes. When extended to Open World Object Detection (OWOD), our method shows significant advantages over the current SOTA method, with average improvements of 13.8% and 6.9% in unknown recall on M-OWODB and S-OWODB benchmarks, respectively. Extensive results validate the effectiveness of our uncertainty-aware approach across different open-set scenarios.

翻译：我们致力于解决开放集目标检测（OSOD）这一具有挑战性的问题，其目标是在未标注图像中同时检测已知和未知物体。主要困难在于缺乏对这些未知类别的监督，使得将它们与背景区分开来颇具挑战。现有的OSOD检测器要么未能有效利用，要么未能充分挖掘训练数据中丰富的未标注未知物体，从而限制了其性能。为应对这些局限，我们提出了UADet，一种基于不确定性的开放集目标检测器，它同时考虑了外观不确定性和几何不确定性。通过整合这些不确定性度量，UADet有效减少了先前方法错误利用或遗漏的未标注实例数量。在OSOD基准测试上的大量实验表明，UADet在检测已知和未知物体方面显著优于先前的最先进（SOTA）方法，在保持已知类别高性能的同时，将未知类别的召回率提升了1.8倍。当扩展到开放世界目标检测（OWOD）任务时，我们的方法相较于当前SOTA方法展现出显著优势，在M-OWODB和S-OWODB基准测试上，未知类别的平均召回率分别提升了13.8%和6.9%。大量结果验证了我们基于不确定性的方法在不同开放集场景下的有效性。