34 citations · 43 across the 8 of their papers we have counts for
8 papers
Dynamic Position Transformation and Boundary Refinement Network for Left Atrial Segmentation
Fangqiang Xu, Wenxuan Tu, Fan Feng +4
Left atrial (LA) segmentation is a crucial technique for irregular heartbeat (i.e., atrial fibrillation) diagnosis. Most current methods for LA segmentation strictly assume that th…
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification
Meng Liu, Ke Liang, Dayu Hu +6
Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-mod…
RARE: Robust Masked Graph Autoencoder
Wenxuan Tu, Qing Liao, Sihang Zhou +5
Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts perf…
GANN: Graph Alignment Neural Network for Semi-Supervised Learning
Linxuan Song, Wenxuan Tu, Sihang Zhou +2
Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labe…
Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network
Wenxuan Tu, Bin Xiao, Xinwang Liu +3
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer f…
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou +7
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample m…