The recently proposed open-world object and open-set detection have achieved a breakthrough in finding never-seen-before objects and distinguishing them from known ones. However, their studies on knowledge transfer from known classes to unknown ones are not deep enough, resulting in the scanty capability for detecting unknowns hidden in the background. In this paper, we propose the unknown sniffer (UnSniffer) to find both unknown and known objects. Firstly, the generalized object confidence (GOC) score is introduced, which only uses known samples for supervision and avoids improper suppression of unknowns in the background. Significantly, such confidence score learned from known objects can be generalized to unknown ones. Additionally, we propose a negative energy suppression loss to further suppress the non-object samples in the background. Next, the best box of each unknown is hard to obtain during inference due to lacking their semantic information in training. To solve this issue, we introduce a graph-based determination scheme to replace hand-designed non-maximum suppression (NMS) post-processing. Finally, we present the Unknown Object Detection Benchmark, the first publicly benchmark that encompasses precision evaluation for unknown detection to our knowledge. Experiments show that our method is far better than the existing state-of-the-art methods.

翻译：近期提出的开放世界物体检测和开放集检测方法在发现前所未见物体并将其与已知物体区分方面取得了突破性进展。然而，这些方法在从已知类别向未知类别迁移知识的研究还不够深入，导致检测隐藏在背景中的未知物体的能力不足。本文提出了一种未知物体嗅探器（UnSniffer），旨在同时发现未知和已知物体。首先，引入广义物体置信度（GOC）评分，该评分仅使用已知样本进行监督，避免对背景中的未知物体进行不当抑制。值得注意的是，这种从已知物体学习到的置信度评分可泛化至未知物体。此外，我们提出负能量抑制损失函数，进一步抑制背景中的非物体样本。其次，由于训练过程中缺乏未知物体的语义信息，推理时难以获取其最优边界框。为解决此问题，我们引入基于图的判定方案，替代人工设计的非极大值抑制（NMS）后处理。最后，我们提出未知物体检测基准（Unknown Object Detection Benchmark），据我们所知，这是首个包含未知检测精度评估的公开基准。实验结果表明，本方法显著优于现有最先进方法。