Call for papers Manifold Learning from Euclid to Riemann ICPR Workshop

September 8th, 2020 Daniela Lopez de Luise

We are organizing the Manifold Learning from Euclid to Riemann Workshop In Conjunction with the 25th International Conference On Pattern Recognition, Milan, Italy 10 – 15 January 2021

We encourage discussions on recent advances, ongoing developments, and novel applications of manifold learning, optimization, feature representations and deep learning techniques. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

Theoretical Advances related to manifold learning such as

·  Dimensionality Reduction (e.g., Locally Linear Embedding, Laplacian Eigenmaps and etc.)

·  Clustering (e.g., discriminative clustering)

·  Kernel methods

·  Metric Learning

·  Time series on non-linear manifolds

·  Transfert learning on non-linear manifolds

·  Generative Models on non-linear manifolds

·  Subspace Methods (e.g., Subspace clustering)

·  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

Applications: 

·  Biometrics

·  Image/video recognition

·  Action/activity recognition

·  Facial expressions recognition

·  Learning and scene understanding

·  Medical imaging

·  Robotics

·  Other related topics not listed above

Special Issue: We will also invite selected papers for submission to a special issue on
Learning with Manifolds  in computer vision in IMAGE AND VISION COMPUTING Journal. https://www.journals.elsevier.com/image-and-vision-computing/call-for-papers/special-issue-on-learning-with-manifolds-in-computer-vision

Important Dates

• Workshop submission deadline: October 10th

• Workshop author notification: November 10th

• Camera-ready submission: November 15th

• Finalized workshop program: December 1st