3 papers
cs.CV2026
Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models
Shaotian Li, Shangze Li, Chuancheng Shi +5
Large-scale vision-language models (VLMs) exhibit remarkable zero-shot capabilities, yet the internal mechanisms driving their anomaly detection (AD) performance remain poorly unde…
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…
cs.CV2025
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…