3 papers
cs.CV2026
SPK: Eliciting Structured Prior Knowledge for Interpretable Out-of-Distribution Detection in Real-Time Object Detection
Changshun Wu, Weicheng He, Xiaowei Huang +1
Object detectors often produce over-confident predictions for objects outside their training categories, leading to so-called out-of-distribution (OoD) hallucinations. Existing app…
cs.CV2025
Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm
Changshun Wu, Weicheng He, Chih-Hong Cheng +2
Out-of-distribution (OoD) inputs pose a persistent challenge to deep learning models, often triggering overconfident predictions on non-target objects. While prior work has primari…
cs.CV2024
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection
Changshun Wu, Weicheng He, Chih-Hong Cheng +2
Out-of-distribution (OoD) detection techniques for deep neural networks (DNNs) become crucial thanks to their filtering of abnormal inputs, especially when DNNs are used in safety-…