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Found 15 papers, 7 papers with code
FedSheafHN: Personalized Federated Learning on Graph-structured Data
Personalized subgraph Federated Learning (FL) is a task that customizes Graph Neural Networks (GNNs) to individual client needs, accommodating diverse data distributions.
PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer
Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics.
Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study
In this work, we rigorously investigate the robustness of graph neural fractional-order differential equation (FDE) models.
PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features and position embeddings for point clouds.
DistilVPR: Cross-Modal Knowledge Distillation for Visual Place Recognition
The utilization of multi-modal sensor data in visual place recognition (VPR) has demonstrated enhanced performance compared to single-modal counterparts.
Image Patch-Matching with Graph-Based Learning in Street Scenes
Matching landmark patches from a real-time image captured by an on-vehicle camera with landmark patches in an image database plays an important role in various computer perception tasks for autonomous driving.
RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging Environments
For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing.
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology.
Graph Neural Convection-Diffusion with Heterophily
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 graphs.
HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion
LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision.
Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks
Graph neural networks (GNNs) have achieved success in various inference tasks on graph-structured data.
RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments
Experiments demonstrate that RobustLoc surpasses current state-of-the-art camera pose regression models and achieves robust performance in various environments.
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Building Facade Parsing R-CNN
Building facade parsing, which predicts pixel-level labels for building facades, has applications in computer vision perception for autonomous vehicle (AV) driving.
From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks
The information metric, e. g. Kullback-Leibler divergence in the original GAN, makes the objective function have different sensitivity on different event probability, which provides an opportunity to refine GAN-based anomaly detection by influencing data generation.
MIM-Based GAN: Information Metric to Amplify Small Probability Events Importance in Generative Adversarial Networks
As for the original GAN, there exist drawbacks for its hidden information measure based on KL divergence on rare events generation and training performance for adversarial networks.
