3 papers
stat.ML2025
A Multi-dimensional Semantic Surprise Framework Based on Low-Entropy Semantic Manifolds for Fine-Grained Out-of-Distribution Detection
Ningkang Peng, Yuzhe Mao, Yuhao Zhang +5
Out-of-Distribution (OOD) detection is a cornerstone for the safe deployment of AI systems in the open world. However, existing methods treat OOD detection as a binary classificati…
cs.CV2025
DFEN: Dual Feature Equalization Network for Medical Image Segmentation
Jianjian Yin, Yi Chen, Chengyu Li +3
Current methods for medical image segmentation primarily focus on extracting contextual feature information from the perspective of the whole image. While these methods have shown…
cs.CV2024
Uncertainty-Participation Context Consistency Learning for Semi-supervised Semantic Segmentation
Jianjian Yin, Yi Chen, Zhichao Zheng +2
Semi-supervised semantic segmentation has attracted considerable attention for its ability to mitigate the reliance on extensive labeled data. However, existing consistency regular…