标题： SFUOD：无源未知物体检测 显示英文标题

标题： SFUOD: Source-Free Unknown Object Detection

摘要： 无源目标检测将在一个源域上预训练的检测器适应到一个未标记的目标域，而无需访问标记的源数据。 虽然这种设置在实践中很有用，因为它消除了在领域自适应期间对源数据集的需求，但它在严格的假设下运行，即只有源域中预定义的对象存在于目标域中。 这种封闭集设置阻止检测器检测未定义的对象。 为了缓解这个假设，我们提出了无源未知对象检测（SFUOD），一种新的场景，使检测器不仅能够识别已知对象，还能将未定义对象检测为未知对象。 为此，我们提出了CollaPAUL（协作调优和基于主轴的未知标签），一种用于SFUOD的新框架。 协作调优通过跨域注意力机制，将辅助编码器的目标相关知识与预训练检测器的源相关知识相结合，以增强知识适应。 此外，基于主轴的未知标签通过主轴投影估计对象性并从模型预测中获取置信度分数，为未知对象分配伪标签。 所提出的CollaPAUL在SFUOD基准测试中达到了最先进的性能，广泛的实验验证了其有效性。 显示英文摘要

摘要： Source-free object detection adapts a detector pre-trained on a source domain to an unlabeled target domain without requiring access to labeled source data. While this setting is practical as it eliminates the need for the source dataset during domain adaptation, it operates under the restrictive assumption that only pre-defined objects from the source domain exist in the target domain. This closed-set setting prevents the detector from detecting undefined objects. To ease this assumption, we propose Source-Free Unknown Object Detection (SFUOD), a novel scenario which enables the detector to not only recognize known objects but also detect undefined objects as unknown objects. To this end, we propose CollaPAUL (Collaborative tuning and Principal Axis-based Unknown Labeling), a novel framework for SFUOD. Collaborative tuning enhances knowledge adaptation by integrating target-dependent knowledge from the auxiliary encoder with source-dependent knowledge from the pre-trained detector through a cross-domain attention mechanism. Additionally, principal axes-based unknown labeling assigns pseudo-labels to unknown objects by estimating objectness via principal axes projection and confidence scores from model predictions. The proposed CollaPAUL achieves state-of-the-art performances on SFUOD benchmarks, and extensive experiments validate its effectiveness.