3 papers
cs.CV2026
CHILD: Human-in-the-Loop OOD Detection for Safe Clinical Deployment
Jinlun Ye, Kaiyue Lu, Runhe Lai +3
Out-of-distribution (OOD) detection is critical for safe deployment of medical AI systems. Recently, test-time adaptation (TTA) has emerged as a new paradigm for OOD detection, aut…
cs.CV2026
TruthLens: Object Hallucination Detection via Self-Evaluating Truthfulness Scores in LVLMs
Yanqi Wu, Runhe Lai, Xinhua Lu +5
Despite the remarkable progress of large vision language models (LVLMs), object hallucination remains a fundamental challenge that hinders their trustworthy deployment. A key findi…
cs.CV2026
DCAC: Dynamic Class-Aware Cache Creates Stronger Out-of-Distribution Detectors
Yanqi Wu, Qichao Chen, Runhe Lai +5
Out-of-distribution (OOD) detection remains a fundamental challenge for deep neural networks, particularly due to overconfident predictions on unseen OOD samples during testing. We…