no code implementations • 9 May 2023 • Sheng Wan, Dashan Gao, Hanlin Gu, Daning Hu
However, in reality, the number of overlapped users is often very small, thus largely limiting the performance of such approaches.
no code implementations • 11 May 2022 • Wentao Yu, Sheng Wan, Guangyu Li, Jian Yang, Chen Gong
To enhance the feature representation ability, in this paper, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations, which is termed Contrastive Graph Convolutional Network (ConGCN), for HSI classification.
no code implementations • NeurIPS 2021 • Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong
Essentially, our CGPN can enhance the learning performance of GNNs under extremely limited labels by contrastively propagating the limited labels to the entire graph.
no code implementations • 19 Sep 2020 • Sheng Wan, Chen Gong, Shirui Pan, Jie Yang, Jian Yang
Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification.
no code implementations • 15 Sep 2020 • Sheng Wan, Shirui Pan, Jian Yang, Chen Gong
Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph.
no code implementations • 26 Sep 2019 • Sheng Wan, Chen Gong, Ping Zhong, Shirui Pan, Guangyu Li, Jian Yang
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance.
1 code implementation • 14 May 2019 • Sheng Wan, Chen Gong, Ping Zhong, Bo Du, Lefei Zhang, Jian Yang
To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.