4 papers · 1 filter
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…
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.…
Lite-SAM Is Actually What You Need for Segment Everything
Jianhai Fu, Yuanjie Yu, Ningchuan Li +5
This paper introduces Lite-SAM, an efficient end-to-end solution for the SegEvery task designed to reduce computational costs and redundancy. Lite-SAM is composed of four main comp…