5 papers
Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection
Runzhi Deng, Yundi Hu, Yiming Zhong +5
Large Multimodal Models (LMMs) show strong few-shot generalization, but industrial anomaly detection remains difficult because defects are small, input resolution is limited, and t…
Medical Image Understanding Improves Survival Prediction via Visual Instruction Tuning
Xixi Liu, Jorge Lazo, Andreas Hallqvist +8
Accurate prognostication and risk estimation are essential for guiding clinical decision-making and optimizing patient management. While radiologist-assessed features from CT scans…
ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection
Runzhi Deng, Yundi Hu, Xinshuang Zhang +5
Few-shot multi-class industrial anomaly detection identifies diverse defects across multiple categories using a single unified model and limited normal samples. Although vision-lan…
Enhancing Out-of-Distribution Detection with Extended Logit Normalization
Yifan Ding, Xixi Liu, Jonas Unger +1
\noindent Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning models. Extensive work has focused on devising various scoring functions for…
Energy-Guided Decoding for Object Hallucination Mitigation
Xixi Liu, Ailin Deng, Christopher Zach
Mitigating object hallucination in large vision-language models (LVLMs) is critical to their safe deployment. Existing methods either are restricted to specific decoding methods, o…