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Found 13 papers, 6 papers with code
Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction
Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting.
Source-Guided Similarity Preservation for Online Person Re-Identification
Our framework is based on the extraction of a support set composed of source images that maximizes the similarity with the target data.
Online unsupervised domain adaptation
Person Re-Identification
Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals
Within the context of Graph Signal Processing (GSP), Graph Learning (GL) is concerned with the inference of the graph's underlying structure from nodal observations.
Inductive Graph Neural Networks for Moving Object Segmentation
Our proposed algorithm enables the deployment of graph-based MOS models in real-world applications.
Time-varying Signals Recovery via Graph Neural Networks
The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series.
Higher-order Sparse Convolutions in Graph Neural Networks
Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks
Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains.
Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation
Our algorithm constructs spatial and k-Nearest Neighbor (k-NN) graphs from the images in the dataset to generate the hypergraphs.
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos
Moving Object Detection (MOD) is a fundamental step for many computer vision applications.
Reconstruction of Time-varying Graph Signals via Sobolev Smoothness
In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples.
Moving Object Detection for Event-based vision using Graph Spectral Clustering
However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution.
Ranked #1 on
Moving Object Detection
on DVSMOTION20
On the Minimization of Sobolev Norms of Time-Varying Graph Signals: Estimation of New Coronavirus Disease 2019 Cases
To this end, we proposed a new method based on the minimization of the Sobolev norm in graph signal processing.
GraphBGS: Background Subtraction via Recovery of Graph Signals
Several deep learning methods for background subtraction have been proposed in the literature with competitive performances.
