Learning with Manifolds in Computer Vision Call for Papers

Learning with Manifolds in Computer Vision
Guest Editors
Mohamed Daoudi, IMT Lille Douai, CRIStAL, France
Mehrtash Harandi, Monash University, Australia
Vittorio Murino, University of Verona, Verona, Italy, and Huawei
  Technologies Ltd., Ireland Research Center, Dublin, Ireland
Paper submission due:  January 31st, 2021
First Notification: April 31st, 2021
Revision: Final Decision:  August 31st, 2021 
Publication: 2021 (tentative)

Aim and Scope
Manifold Learning (ML) has been the subject of intensive study over
the past two decades in the computer vision and machine learning
communities. Originally, manifold learning techniques aim to identify
the underlying structure (usually low-dimensional) of data from a set
of, typically high-dimensional, observations. The recent advances in
deep learning make one wonder whether data-driven learning techniques
can benefit from the theoretical findings from ML studies. This
innocent looking question becomes more important if we note that deep
learning techniques are notorious for being data-hungry and (mostly)
supervised. On the contrary, many ML techniques unravel data
structures without much supervision. This special issue aims at
raising the question of how classical ML techniques can help deep
learning and vice versa, and targets works and studies investigating
how to bridge the gap.

Besides, the use of Riemannian geometry in tackling/modelling various
problems in computer vision has seen a surge of interest recently. The
benefits of geometrical thinking can be understood by noting that in
many applications, data naturally lies on smooth manifolds, hence
distances and similarity measures computed by considering the geometry
of the space naturally result in better and more accurate
modelling. Various studies demonstrate the benefits of geometrical
techniques in analysing images and videos such as face recognition,
activity classification, object detection and classification, and
structure from motion to name a few.

This special issue addresses challenges and future directions related
to the application of non-linear manifold and machine learning in
computer vision.

Topics and Guidelines

This special issue targets researchers and practitioners from both
industry and academia to provide a forum in which to publish recent
state-of-the-art achievements in Non-Euclidean geometry and machine
learning for computer vision. Topics of interest include, but are not
limited to:
?      Theoretical Advances related to manifold learning
?      Dimensionality Reduction (e.g., Locally Linear Embedding, Laplacian Eigenmaps)
?      Clustering
?      Kernel methods
?      Metric Learning
?      Time series on non-linear manifolds
?      Transfer learning on non-linear manifolds
?      Generative Models on non-linear manifolds
?      Subspace Methods
?      Advanced Optimization Techniques (constrained and non-convex optimization techniques on non-linear manifolds)
?      Mathematical Models for learning sequences
?      Mathematical Models for learning Shapes
?      Deep learning and non-linear manifolds
?      Low-rank factorization methods
?      Graph-based Analysis
?      Learning via Hyperbolic geometry
And related applications in computer vision (a non-exhaustive list in provided below):
?      Face recognition
?      Image/video analysis and classification
?      Action/activity recognition
?      Behavior analysis
?      Facial expressions recognition
?      Person Re-Identification
?      Face generation
?      Facial expression generation
?      Fine-grained recognition
?      Visual inspection