First International Workshop on

"Graph Learning and Graph Signal Processing Algorithms in Computer Vision (G2SP-CV 2024)"

December 01, 2024, Kolkata, India

(To be held in conjunction with ICPR 2024)

Workshop Description

Graph representation learning and its applications have gained significant attention in recent years. Notably, Graph Signal Processing (GSP) and Graph Neural Networks (GNNs) have been extensively studied. GSP extends the concepts of classical digital signal processing to signals supported on graphs. Similarly, GNNs extend the concepts of Convolutional Neural Networks (CNNs) to non-Euclidean data modeled as graphs. GSP and GNNs have numerous applications such as semi-supervised learning, point cloud semantic segmentation, prediction of individual relations in social networks, image, and video processing. Early GSP researchers explored low-dimensional representations of high-dimensional data via spectral graph theory, i.e., mathematical analysis of eigen-structures of the adjacency and graph Laplacian matrices. Researchers first developed algorithms for low-level tasks such as signal compression, wavelet decomposition, filter banks on graphs, regression, and denoising, motivated by data collected from distributed sensor networks. Soon, researchers widened their scope and studied GSP techniques for image applications (image filtering, segmentation) and computer graphics. More recently, GSP tools were extended to video processing tasks such as moving object segmentation, demonstrating its potential in a wide range of computer vision problems.

GNNs have shown great potential in computer vision applications such as point cloud semantic segmentation, video understanding, and event-based vision. However, designing GSP or GNN algorithms for specific computer vision tasks has several practical challenges such as spatio-temporal constraints, time-varying models, and real-time implementations. Indeed, the computational complexity of many existing GSP/GNN algorithms at present for very large graphs is currently one limitation. In semi-supervised learning, GSP-based classifiers provide clear interpretations from a graph spectral perspective when propagating label information from known to unknown nodes. However, centralized graph spectral algorithms are slow and no fast-distributed graph labeling algorithms are known to perform well. In that sense, research is required in the development of fast GSP/GNN tools to be competitive against well-established deep learning methods like CNNs.

Endorsement

This workshop is endorsed by IAPR TC-15 on Graph-Based Representations.

https://www.iapr-tc15.org/

Goal of this workshop

The goals of this workshop are thus three-fold:

Designing GSP/GNNs methods for pattern recognition and computer vision applications.
Proposing new algorithms that can fulfil the requirements of real-time applications.
Proposing robust and interpretable algorithms to handle the key challenges in pattern recognition and computer vision applications.

Broad subject areas for submitting papers

Graph Machine Learning for Computer Vision
Graph Neural Networks (GNNs)
GNN Architectures
Interpretable/Explainable GNNs
Unsupervised/Self-Supervised GNNs
GSP-based Graph Learning in GNNs
Sampling and Recovery of Graph Signals
Statistical Graph Signal Processing

Non-linear Graph Signal Processing
Signals in high-order Graphs
Graph-based Segmentation and Classification
Graph-based Image and Video Processing
Graph-based Image Restoration
Graph-based Image Filtering
Graph-based Event Data Processing

Important Dates

Full Paper Submission Deadline: September 1, 2024

Decisions to Authors: September 15, 2024

Camera-ready Deadline: September 27, 2024

Selected papers, after extensions and further revisions, will be published in a special issue of an international journal.

Paper Submission

The link for the papers submission is https://cmt3.research.microsoft.com/G2SPCV2024

Please read the following instructions before submission

Paper Format and Length
Springer LNCS format with maximum 15 pages (including references) during paper submission. To take care of reviewers' comments, one more page is allowed (without any charge) during revised/camera ready submission. Moreover, authors may purchase up to 2 extra pages. Extra page charges must be paid at the time of registration.

Springer LNCS paper formatting instructions and templates for ICPR-2024 are available here DOC and Latex
For Supplementary materials, click on Instruction.

Please submit at CMT3 G2SPCV2024 (linked to https://cmt3.research.microsoft.com/G2SPCV2024)