SPK: Eliciting Structured Prior Knowledge for Interpretable Out-of-Distribution Detection in Real-Time Object Detection
Summary
Structured Prior Knowledge (SPK) is a framework that explicitly extracts latent semantic, geometric, and contextual priors from pretrained object detectors to achieve state-of-the-art out-of-distribution detection, improving interpretability and reliability.
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Paper page - SPK: Eliciting Structured Prior Knowledge for Interpretable Out-of-Distribution Detection in Real-Time Object Detection
Source: https://huggingface.co/papers/2608.19080
Abstract
Structured Prior Knowledge explicitly extracts latent semantic, geometric, and contextual priors from pretrained object detectors to detect out-of-distribution hallucinations via a compact interpretable representation.
Object detectorsoften 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 suchhallucinationstypically either construct scoring functions directly over learned object detector representations or modify the object detector itself to suppress hallucination emergence. However, thelatent priorsimplicitly encoded in these representations remain largely unexplored and have not been explicitly decoded for OoD detection. To uncover and exploit theselatent priors, we proposeStructured Prior Knowledge(SPK), a hallucination-oriented framework that explicitly elicits OoD-relevant priors from pretrainedobject detectors. Specifically, SPK leverages in-distribution data and hallucination-inducing samples as diagnostic supervision to elicitpart-level semantic conceptsunderlying object detector decision-making, rather than using them merely for rejection or object detector adaptation. The elicited semantic priors are further integrated with geometric andcontextual priorsto 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 pretrainedobject detectorsalready 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 leveraginglatent priors. Code and data are available at: https://gricad-gitlab.univ-grenoble-alpes.fr/dnn-safety/spk
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