SPK：在实时目标检测中获取结构化先验知识用于可解释的分布外检测

摘要

目标检测器常常对其训练类别之外的目标产生过度自信的预测，从而导致所谓的分布外（OoD）幻觉。现有的检测或缓解这类幻觉的方法通常要么直接在习得的目标检测器表示上构建评分函数，要么修改目标检测器本身以抑制幻觉的产生。然而，这些表示中隐式编码的潜在先验在很大程度上仍未得到探索，也尚未被显式解码用于OoD检测。为了揭示并利用这些潜在先验，我们提出了结构化先验知识（SPK），一种面向幻觉的框架，能够从预训练目标检测器中显式地引出与OoD相关的先验。具体而言，SPK利用分布内数据和诱发幻觉的样本作为诊断性监督，以引出支撑目标检测器决策的部件级语义概念，而非仅仅将其用于拒绝或目标检测器适配。所引出的语义先验进一步与几何先验和上下文先验相结合，形成一种用于OoD检测的紧凑五维SPK表示。在多种目标检测器架构和多个OoD基准上的大量实验表明，SPK实现了最先进的OoD检测性能。我们的研究结果揭示，预训练目标检测器已经编码了远比通常用于OoD检测所利用的知识更丰富的潜在知识。更重要的是，这些知识可以被显式地引出，并组织成一个紧凑、结构化且可解释的知识空间，用于预测可靠性分析。这为通过显式揭示并利用潜在先验来提高目标检测器可靠性提供了一条有前景的主动路径。代码和数据可在以下网址获取：https://gricad-gitlab.univ-grenoble-alpes.fr/dnn-safety/spk

English

Object detectors often produce over-confident predictions for objects outside their training categories, leading to so-called out-of-distribution (OoD) hallucinations. Existing approaches for detecting or mitigating such hallucinations typically either construct scoring functions directly over learned object detector representations or modify the object detector itself to suppress hallucination emergence. However, the latent priors implicitly encoded in these representations remain largely unexplored and have not been explicitly decoded for OoD detection. To uncover and exploit these latent priors, we propose Structured Prior Knowledge (SPK), a hallucination-oriented framework that explicitly elicits OoD-relevant priors from pretrained object detectors. Specifically, SPK leverages in-distribution data and hallucination-inducing samples as diagnostic supervision to elicit part-level semantic concepts underlying object detector decision-making, rather than using them merely for rejection or object detector adaptation. The elicited semantic priors are further integrated with geometric and contextual priors to form a compact five-dimensional SPK representation for OoD detection. Extensive experiments across diverse object detector architectures and multiple OoD benchmarks demonstrate that SPK achieves state-of-the-art OoD detection. Our findings reveal that pretrained object detectors already encode substantially richer latent knowledge than is typically exploited for OoD detection. More importantly, this knowledge can be explicitly elicited and organized into a compact, structured, and interpretable knowledge space for prediction reliability analysis. This suggests a promising proactive route for improving object detector reliability by explicitly uncovering and leveraging latent priors. Code and data are available at: https://gricad-gitlab.univ-grenoble-alpes.fr/dnn-safety/spk