8 papers
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…
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…
Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models
Runhe Lai, Xinhua Lu, Yanqi Wu +3
Multimodal large language models (MLLMs) have achieved remarkable progress, yet the object hallucination remains a critical challenge for reliable deployment. In this paper, we pre…
TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models
Jinlun Ye, Jiang Liao, Runhe Lai +4
Vision-language models (VLMs) such as CLIP exhibit strong Out-of-distribution (OOD) detection capabilities by aligning visual and textual representations. Recent CLIP-based test-ti…
DynProto: Dynamic Prototype Evolution for Out-of-Distribution Detection
Yanqi Wu, Xinhua Lu, Runhe Lai +4
Recent studies show that using potential out-of-distribution (OOD) labels from large corpora as auxiliary information can improve OOD detection in vision-language models (VLMs). Ho…
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…
Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detection
Runhe Lai, Xinhua Lu, Kanghao Chen +3
In trustworthy medical diagnosis systems, integrating out-of-distribution (OOD) detection aims to identify unknown diseases in samples, thereby mitigating the risk of misdiagnosis.…
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
Xinhua Lu, Runhe Lai, Yanqi Wu +3
Pre-trained vision-language models (VLMs) have advanced out-of-distribution (OOD) detection recently. However, existing CLIP-based methods often focus on learning OOD-related knowl…