Search Results for author:
Found 5 papers, 3 papers with code
A Generalized Neural Diffusion Framework on Graphs
In this paper, we propose a general diffusion equation framework with the fidelity term, which formally establishes the relationship between the diffusion process with more GNNs.
Single-pixel imaging based on deep learning
Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors.
Quaternion-valued Correlation Learning for Few-Shot Semantic Segmentation
In this paper, we introduce a quaternion perspective on correlation learning and propose a novel Quaternion-valued Correlation Learning Network (QCLNet), with the aim to alleviate the computational burden of high-dimensional correlation tensor and explore internal latent interaction between query and support images by leveraging operations defined by the established quaternion algebra.
Ranked #19 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset.
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration
Specifically, we first verify that the confidence distribution in a graph has homophily property, and this finding inspires us to design a calibration GNN model (CaGCN) to learn the calibration function.
