no code implementations • 29 May 2024 • Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo

Graph Neural Networks (GNNs) have advanced spatiotemporal forecasting by leveraging relational inductive biases among sensors (or any other measuring scheme) represented as nodes in a graph.

no code implementations • 29 May 2024 • Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo

Processing multidomain data defined on multiple graphs holds significant potential in various practical applications in computer science.

1 code implementation • 28 Mar 2024 • Jhon A. Castro-Correa, Jhony H. Giraldo, Mohsen Badiey, Fragkiskos D. Malliaros

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.

1 code implementation • 23 Feb 2024 • Hamza Rami, Jhony H. Giraldo, Nicolas Winckler, Stéphane Lathuilière

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

no code implementations • 29 Oct 2023 • Mohamad H. Alizade, Aref Einizade, Jhony H. Giraldo

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.

no code implementations • 16 May 2023 • Wieke Prummel, Jhony H. Giraldo, Anastasia Zakharova, Thierry Bouwmans

Our proposed algorithm enables the deployment of graph-based MOS models in real-world applications.

no code implementations • 22 Feb 2023 • Jhon A. Castro-Correa, Jhony H. Giraldo, Anindya Mondal, Mohsen Badiey, Thierry Bouwmans, Fragkiskos D. Malliaros

The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series.

no code implementations • 21 Feb 2023 • Jhony H. Giraldo, Sajid Javed, Arif Mahmood, Fragkiskos D. Malliaros, Thierry Bouwmans

Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.

1 code implementation • 5 Dec 2022 • Jhony H. Giraldo, Konstantinos Skianis, Thierry Bouwmans, Fragkiskos D. Malliaros

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.

no code implementations • 11 Oct 2022 • Jhony H. Giraldo, Vincenzo Scarrica, Antonino Staiano, Francesco Camastra, Thierry Bouwmans

Our algorithm constructs spatial and k-Nearest Neighbor (k-NN) graphs from the images in the dataset to generate the hypergraphs.

1 code implementation • 13 Jul 2022 • Jhony H. Giraldo, Arif Mahmood, Belmar Garcia-Garcia, Dorina Thanou, Thierry Bouwmans

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.

no code implementations • 13 Jul 2022 • Jhony H. Giraldo, Sajid Javed, Naoufel Werghi, Thierry Bouwmans

Moving Object Detection (MOD) is a fundamental step for many computer vision applications.

1 code implementation • International Conference on Computer Vision Workshops 2021 • Anindya Mondal, Shashant R, Jhony H. Giraldo, Thierry Bouwmans, Ananda S. Chowdhury

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

1 code implementation • 1 Jul 2020 • Jhony H. Giraldo, Thierry Bouwmans

To this end, we proposed a new method based on the minimization of the Sobolev norm in graph signal processing.

no code implementations • 17 Jan 2020 • Jhony H. Giraldo, Thierry Bouwmans

Several deep learning methods for background subtraction have been proposed in the literature with competitive performances.

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