35 citations · 95 across the 15 of their papers we have counts for
7 papers · 1 filter
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…