9 citations · 9 across the 6 of their papers we have counts for
6 papers
Dynamic Heterogeneous Graph Representation Learning: A Survey
Huan Liu, Pengfei Jiao, Jie Yin +2
Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest as evolving heterogeneous entit…
TriCons-Pose: Triangle-Invariant Geometric Consistency Learning for Category-Level Object Pose Estimation
Zuzhi Yang, Bingtao Ma, Shuai Wang +5
Category-level object pose estimation is a crucial yet challenging task in both academia and industry, and has achieved remarkable success by leveraging keypoint-based corresponden…
TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism
Hongjiang Chen, Pengfei Jiao, Ming Du +4
The growing interest in Temporal Graph Neural Networks (TGNNs) stems from their ability to model complex dynamics and deliver superior performance. However, TGNNs encounter fundame…
ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
Hongjiang Chen, Xin Zheng, Pengfei Jiao +5
Temporal graph neural networks (TGNNs) have gained significant traction for solving real-world temporal graph tasks. However, their interpretability remains limited, as most TGNNs…
MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries
Shuai Wang, Tengjin Weng, Jingyi Wang +6
Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multi…
Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learning
Tengjin Weng, Yang Shen, Zhidong Zhao +2
Optic disc and cup segmentation plays a crucial role in automating the screening and diagnosis of optic glaucoma. While data-driven convolutional neural networks (CNNs) show promis…