First Workshop on When Graph Signal Processing meets Computer Vision Call for Papers

First Workshop on When Graph Signal Processing meets Computer Vision

Graph signal processing (GSP) [is the study of computational tools to
process and analyze data residing on irregular correlation structures
described by graphs. Early GSP researchers explored low-dimensional
representations of high-dimensional data via spectral graph
theory---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. More generally, GSP has been found effective in image
processing tasks (image restoration and denoising, image composition,
image alignment and rectification, multi-focus image fusion, etc),
video processing tasks (tracking, motion saliency, video coding,
background/foreground separation, etc.), and 3D imaging tasks (point
cloud processing, 3D motion recovery, etc). Moreover, GSP can
potentially influence the development of Graph Convolutional Networks
from a theoretical standpoint.

However, designing GSP 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 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 tools to be competitive against deep learning

The goals of this workshop are thus three-fold: 1) designing GSP
methods for 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 computer vision applications.

Papers are solicited to address graph signal processing to be applied
in computer vision, including but not limited to the followings:

Sampling and Recovery of Graph Signals

Statistical Graph Signal Processing

Non-linear Graph Signal Processing

Signals in high-order Graphs

Graph-based Image Restoration

Graph-based  Image Filtering

Graph-based  Segmentation and  Classification

Graph-based Image and Video Processing

Graph Convolutional Networks.(GNNs)

Interpretable/Explainable GNNs

Unsupervised/Self-Supervised GNNs

GSP-based Graph Learning in GNNs


Full Paper Submission Deadline: July 13, 2021 (for papers not
submitted at ICCV, 
July 25, 2021 (for papers that are awaiting for
ICCV decisions)

Decisions to Authors: July 31, 2021

Camera-ready Deadline:  August 17, 2021

Main organizers

Thierry Bouwmans, Associate Professor, Laboratoire MIA, Univ. La
Rochelle, France.

Gene Cheung, Associate Professor, Department of EECS, York University,
Toronto, Canada.

Wei Hu, Wangxuan Institute of Computer Technology Peking University,
Beijing, China.

Yuichi Tanaka, Tokyo University of Agriculture and Technology, Japan.

Laura Toni, University College London, UK.