Graph Learning and Graph Signal Processing Algorithms in Computer Vision (G2SP-CV 2024) Call for Papers

Call for Paper for the First International Workshop on 
Graph Learning and Graph Signal Processing Algorithms in Computer Vision (G2SP-CV 2024) 

n conjunction with ICPR 2024, Kolkata, India, December 1, 2024.

Description of Topic

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.

From the GNN side, Bruna et al. proposed the first modern GNN by
extending the convolutional operator of CNNs to graphs. Later,
researchers used the concepts of GSP to propose localized spectral
filtering. Subsequently, Kipf and Welling approximated the filtering
operation of spectral filtering to perform efficient convolution
operations on graph. Other major GNN works include the study of
inductive representation learning on graphs and the development of
graph attention networks. 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.

The goals of this workshop are thus three-fold: 1) designing GSP/GNNs
methods for pattern recognition and computer vision applications; 2)
proposing new adaptive and incremental algorithms that reach the
requirements of real-time applications; and 3) proposing robust and
interpretable algorithms to handle the key challenges in pattern
recognition and computer vision applications.

Papers are solicited to address GSP/GNNs to be applied in computer
vision, including but not limited to the following:

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

 

Main Organizers

Thierry Bouwmans, Associate Professor (HDR), Laboratoire MIA, La
Rochelle UniversitÚ, France.

Jhony H. Giraldo, Assistant Professor, LTCI, TÚlÚcom Paris,
France.

Ananda S. Chowdhury, Professor, Jadavpur University, India.

Badri N. Subudhi, Associate Professor, Indian Institute of Technology
Jammu, India.


Important Dates 

Full Paper Submission Deadline: July 30, 2024

Decisions to Authors:                  September 1, 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.