Unknown Object Detection (UOD) aims to identify objects of unseen categories, differing from the traditional detection paradigm limited by the closed-world assumption. A key component of UOD is learning a generalized representation, i.e. objectness for both known and unknown categories to distinguish and localize objects from the background in a class-agnostic manner. However, previous methods obtain supervision signals for learning objectness in isolation from either localization or classification information, leading to poor performance for UOD. To address this issue, we propose a transformer-based UOD framework, UN-DETR. Based on this, we craft Instance Presence Score (IPS) to represent the probability of an object's presence. For the purpose of information complementarity, IPS employs a strategy of joint supervised learning, integrating attributes representing general objectness from the positional and the categorical latent space as supervision signals. To enhance IPS learning, we introduce a one-to-many assignment strategy to incorporate more supervision. Then, we propose Unbiased Query Selection to provide premium initial query vectors for the decoder. Additionally, we propose an IPS-guided post-process strategy to filter redundant boxes and correct classification predictions for known and unknown objects. Finally, we pretrain the entire UN-DETR in an unsupervised manner, in order to obtain objectness prior. Our UN-DETR is comprehensively evaluated on multiple UOD and known detection benchmarks, demonstrating its effectiveness and achieving state-of-the-art performance.

翻译：未知目标检测旨在识别未见类别的目标，这不同于受限于封闭世界假设的传统检测范式。UOD的一个关键组成部分是学习一种广义表示，即针对已知和未知类别的目标性，以类别无关的方式从背景中区分和定位目标。然而，先前的方法从定位或分类信息中孤立地获取用于学习目标性的监督信号，导致UOD性能不佳。为解决此问题，我们提出了一个基于Transformer的UOD框架——UN-DETR。在此基础上，我们设计了实例存在分数来表示目标存在的概率。为了实现信息互补，IPS采用联合监督学习策略，整合来自位置和类别潜在空间中代表通用目标性的属性作为监督信号。为了增强IPS学习，我们引入了一种一对多分配策略以纳入更多监督。接着，我们提出了无偏查询选择，为解码器提供优质的初始查询向量。此外，我们提出了一种IPS引导的后处理策略，以过滤冗余框并修正已知和未知目标的分类预测。最后，我们以无监督方式预训练整个UN-DETR，以获得目标性先验。我们的UN-DETR在多个UOD和已知检测基准上进行了全面评估，证明了其有效性并取得了最先进的性能。