[visionlist] CFP – ICCV 2017 International Workshop on Manifold Learning: from Euclid to Riemann

ICCV 2017 International Workshop on Manifold Learning: from Euclid to Riemann
28 October 2017
In conjunction with ICCV 2017, Venice, Italy

Webpage: http://ift.tt/2sSZFSr
Contact: maniflearn@googlegroups.com

Paper Submission: July 20th , 2017
Author Notification: August 20th 2017
Camera Ready: August 25th , 2017
Workshop: October 28th, 2017

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 observations (in the form of high-dimensional vectors). 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 supervised (mostly). On the contrary, many ML techniques unravel data structures without much supervision. This workshop
considers itself as the frontier to raise the question of how classical ML techniques can help deep learning and vice versa and targets studies and discussions that bridge the gap.

In this workshop, we will explore the latest development in machine learning techniques developed to work on/benefit from the non-linear manifolds. We will also target challenges and future directions related to the application of non-linear geometry, Riemannian manifolds in computer vision and machine learning. This workshop also acts as an opportunity for cross-disciplinary discussions and collaborations.

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
– Hashing
– Feature learning
– Metric Learning
– 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

and Applications, such as:
– Biometrics
– Image/video recognition
– Action/activity recognition
– Facial expressions recognition
– Learning and scene understanding
– Medical imaging
– Robotics

and other related topics not listed above.

Prof. Pierre Absil (Department of Mathematical Engineering, University of Louvain, Belgium)
Home Page: http://ift.tt/2r2Fh4V
Talk Title: Fittingcomposite Bézier curves on manifolds and their applications in computer vision
Abstract: http://ift.tt/2rM03mG

Prof. Cristian Sminchisescu (Mathematical Sciences, Lund University, Sweden)
Home Page: http://ift.tt/2r2iIgQ
Talk Title: Deep Structured Models for Visual Recognition
Abstract: http://ift.tt/2rM03mG

Dr. Minh Ha Quang (IIT Istituto Italiano di Tecnologia, Italy)
Home Page: http://ift.tt/2rMk2BP
Talk Title: Infinite-dimensional covariance operators and their applications in computer vision
Abstract: http://ift.tt/2rM03mG

Dr. Pavan Turaga (Arizona State University, USA)
Home Page: http://ift.tt/2r2FzJj
Talk Title: Geometric Methods in Modeling Dynamical Phenomenon: Applications in Video Analysis
Abstract: http://ift.tt/2rM03mG

Prof. Mohamed Daoudi (IMT Lille Douai, CRIStAL UMR CNRS, France)
Dr. Mehrtash Harandi (Data61-CSIRO and Australian National University, Australia)
Prof. Vittorio Murino (IIT Istituto Italiano di Tecnologia, Italy)
Prof. Richard Hartley (Australian National University and Data61-CSIRO, Australia

All submissions will be handled electronically via the conference’s CMT Website (http://ift.tt/2rMchvs). Submitted papers must be no longer than 8 pages following the official ICCV guideline
ManifLearn reviewing will be double-blind. Each submission will be reviewed by at least three reviewers for originality, significance, clarity, soundness, relevance and technical contents. Papers that are not blind, or do not use the template, or have more than 8 pages (excluding references) will be rejected without review.


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