In Conjunction with ICCV 2017 

About this Workshop

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
  • Applications: 
    • Biometrics
    • Image/video recognition
    • Action/activity recognition
    • Facial expressions recognition
    • Learning and scene understanding
    • Medical imaging
    • Robotics
    • Other related topics not listed above


  • The Workshop of  Manifold learning: from Euclid to Riemann is accepted as a full day workshop in conjunction with the ICCV 2017 conference.

Important dates

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

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