5 papers
Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
Shuaike Xu, Xiaolin Zhang, Peng Zhang +1
Graph node classification with few labeled nodes presents significant challenges due to limited supervision. Conventional methods often exploit the graph in a transductive learning…
: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Qin Zhang, Zelin Shi, Xiaolin Zhang +3
Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when a…
Entropy Neural Estimation for Graph Contrastive Learning
Yixuan Ma, Xiaolin Zhang, Peng Zhang +1
Contrastive learning on graphs aims at extracting distinguishable high-level representations of nodes. In this paper, we theoretically illustrate that the entropy of a dataset can…
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure Network
Zhibo Tain, Xiaolin Zhang, Peng Zhang +1
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples. However, the effect…
Mutual Exclusive Modulator for Long-Tailed Recognition
Haixu Long, Xiaolin Zhang, Yanbin Liu +2
The long-tailed recognition (LTR) is the task of learning high-performance classifiers given extremely imbalanced training samples between categories. Most of the existing works ad…