35 citations · 95 across the 15 of their papers we have counts for
15 papers
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…
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 (…
DistilVPR: Cross-Modal Knowledge Distillation for Visual Place Recognition
Sijie Wang, Rui She, Qiyu Kang +4
The utilization of multi-modal sensor data in visual place recognition (VPR) has demonstrated enhanced performance compared to single-modal counterparts. Nonetheless, integrating a…
RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging Environments
Rui She, Qiyu Kang, Sijie Wang +4
For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing. In various computer percepti…
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
Kai Zhao, Qiyu Kang, Yang Song +3
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology. This paper investigates GNNs derived fr…
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
Qiyu Kang, Kai Zhao, Yang Song +2
In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types. In this paper, we model th…
Graph Neural Convection-Diffusion with Heterophily
Kai Zhao, Qiyu Kang, Yang Song +3
Graph neural networks (GNNs) have shown promising results across various graph learning tasks, but they often assume homophily, which can result in poor performance on heterophilic…
HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion
Sijie Wang, Qiyu Kang, Rui She +4
LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision. LiDAR-based retrieval from a database typically incurs high c…
Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks
Qiyu Kang, Kai Zhao, Yang Song +3
Graph neural networks (GNNs) have achieved success in various inference tasks on graph-structured data. However, common challenges faced by many GNNs in the literature include the…
On the Robustness of Graph Neural Diffusion to Topology Perturbations
Yang Song, Qiyu Kang, Sijie Wang +2
Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing attention recently. The capability of graph neural partial differential equations…
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks
Qiyu Kang, Yang Song, Qinxu Ding +1
Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where malicious human-imperceptible perturbations are included in the input to the deep network…
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks
Yang Song, Qiyu Kang, Wee Peng Tay
Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This pape…