79 citations · 98 across the 10 of their papers we have counts for
10 papers
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
Shuai Zheng, Zhizhe Liu, Zhenfeng Zhu +3
Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. Howe…
Node-oriented Spectral Filtering for Graph Neural Networks
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +2
Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent…
Multi-modal Graph Learning for Disease Prediction
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +4
Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performa…
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning
Zhenyu Guo, Shuai Zheng, Zhizhe Liu +2
Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but…
Multi-modal Graph Learning for Disease Prediction
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +3
Benefiting from the powerful expressive capability of graphs, graph-based approaches have achieved impressive performance in various biomedical applications. Most existing methods…
Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation
Zhizhe Liu, Zhenfeng Zhu, Shuai Zheng +3
To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the f…
Adversarial Graph Disentanglement
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +2
A real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. However, most existing methods lack consideration of t…
Taking Modality-free Human Identification as Zero-shot Learning
Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu +3
Human identification is an important topic in event detection, person tracking, and public security. There have been numerous methods proposed for human identification, such as fac…
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning
Shuai Zheng, Zhenfeng Zhu, Xingxing Zhang +3
Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many compute vision tasks. However, most existing algorit…
Convolutional Prototype Learning for Zero-Shot Recognition
Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu +3
Zero-shot learning (ZSL) has received increasing attention in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. The key to ZSL i…