96 citations · 245 across the 21 of their papers we have counts for
6 papers · 1 filter
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
Yunyang Zhang, Zhiqiang Gong, Xiaohu Zheng +2
As a powerful way of realizing semi-supervised segmentation, the cross supervision method learns cross consistency based on independent ensemble models using abundant unlabeled ima…
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong +3
Few-shot segmentation enables the model to recognize unseen classes with few annotated examples. Most existing methods adopt prototype learning architecture, where support prototyp…
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
Xiaohu Zheng, Wen Yao, Zhiqiang Gong +2
Temperature field reconstruction is essential for analyzing satellite heat reliability. As a representative machine learning model, the deep convolutional neural network (DCNN) is…
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
Xiaohu Zheng, Wen Yao, Zhiqiang Gong +3
For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the convolu…
A deep learning method based on patchwise training for reconstructing temperature field
Xingwen Peng, Xingchen Li, Zhiqiang Gong +2
Physical field reconstruction is highly desirable for the measurement and control of engineering systems. The reconstruction of the temperature field from limited observation plays…
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks
Xu Liu, Wei Peng, Zhiqiang Gong +2
Temperature field inversion of heat-source systems (TFI-HSS) with limited observations is essential to monitor the system health. Although some methods such as interpolation have b…