2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
A High-Frequency Focused Network for Lightweight Single Image Super-Resolution
Xiaotian Weng, Yi Chen, Zhichao Zheng +3
Lightweight neural networks for single-image super-resolution (SISR) tasks have made substantial breakthroughs in recent years. Compared to low-frequency information, high-frequenc…
Multi-Semantic Interactive Learning for Object Detection
Shuxin Wang, Zhichao Zheng, Yanhui Gu +2
Single-branch object detection methods use shared features for localization and classification, yet the shared features are not fit for the two different tasks simultaneously. Mult…
Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation
Jianjian Yin, Zhichao Zheng, Yanhui Gu +2
Existing approaches focus on using class-level features to improve semantic segmentation performance. How to characterize the relationships of intra-class pixels and inter-class pi…