35 citations · 75 across the 16 of their papers we have counts for
7 papers · 1 filter
Distributed-Order Fractional Graph Operating Network
Kai Zhao, Xuhao Li, Qiyu Kang +5
We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fraction…
PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with Image and Point Cloud Fusion
Sijie Wang, Qiyu Kang, Rui She +3
Place recognition plays a crucial role in the fields of robotics and computer vision, finding applications in areas such as autonomous driving, mapping, and localization. Place rec…
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
Qiyu Kang, Kai Zhao, Qinxu Ding +5
We introduce the FRactional-Order graph Neural Dynamical network (FROND), a new continuous graph neural network (GNN) framework. Unlike traditional continuous GNNs that rely on int…
PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer
Rui She, Qiyu Kang, Sijie Wang +7
Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics. However, registration tasks under challe…
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models
Kaikang Zhao, Xi Chen, Wei Huang +3
The integration of an ensemble of deep learning models has been extensively explored to enhance defense against adversarial attacks. The diversity among sub-models increases the at…
PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
Rui She, Sijie Wang, Qiyu Kang +5
Point cloud registration is a crucial technique in 3D computer vision with a wide range of applications. However, this task can be challenging, particularly in large fields of view…
Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study
Qiyu Kang, Kai Zhao, Yang Song +5
In this work, we rigorously investigate the robustness of graph neural fractional-order differential equation (FDE) models. This framework extends beyond traditional graph neural (…