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Introduction

This workshop aims at promoting discussions among researchers investigating innovative tensor-based approaches to computer vision problems. Tensors have been a crucial mathematical object for several applications in computer vision and machine learning. It has been an essential ingredient in modelling latent semantic spaces, higher-order data factorization, and modelling higher-order information in visual data, and has found numerous applications in several hot topics in computer vision including, but not limited to human action recognition, object recognition, and video understanding. Moreover, tensor-based algorithms are increasingly finding significant applications in deep learning. With the rise of big data, tensors may yet prove crucial in both understanding deep architectures, as well as, may aid robust learning and generalization in inference algorithms.

Call for paper

Important date

2017-04-20
Draft paper submission deadline
2017-05-11
Draft paper acceptance notification

Submission Topics

We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

  • Tensor methods in deep learning

  • Supervised learning in computer vision

  • Unsupervised feature learning and multimodal representations

  • Tensors in low-level feature design

  • Mid-level representations with tensor methods

  • Low-rank factorisation methods and denoising approaches

  • Latent topic models using tensor methods

  • Tensors in optimization and dictionary learning

  • Tensor hardware architectures

  • Advancements in multi-linear algebra

  • Riemannian geometry and SPD matrices

  • Applications of tensors for:

  • image/video recognition

  • object recognition

  • scene understanding

  • industrial and medical applications

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Important Date
  • Jul 26

    2017

    Conference Date

  • Apr 20 2017

    Draft paper submission deadline

  • May 11 2017

    Draft Paper Acceptance Notification

  • Jul 26 2017

    Registration deadline