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.
We encourage discussions on recent advances, ongoing developments, and novel applications of multi-linear algebra, optimization, and feature representations using tensors. 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
- Other related topics not listed above
INVITED SPEAKERSBelow is the list of speakers who have kindly agreed (tentatively) to give talks during the workshop:
- Dr. Andrzej Cichocki (Brain Science Institute RIKEN and SKOLTECH)
Title: Tensor Networks for Deep Learning.
- Prof. Animashree Anandkumar (University of California Irvine)
Title: Tensor methods for large-scale learning.
- Dr. Ivan Oseledets (Skolkovo Institute of Science and Technology)
Title: Deep learning and tensors for the approximation of multivariate functions: recent results and open problems.
- Dr. Lieven De Lathauwer (KU Leuven) Title: Numerical optimization algorithm for tensor-based recognition.
- Dr. Lior Horesh (IBM T.J. Watson Research Center and Columbia University)
Title: A New Tensor Algebra - Theory and Applications.
- Prof. Luc Florack (Eindhoven University of Technology)
Title: Redeeming the Clinical Promise of Diffusion MRI in Support of the Neurosurgical Workflow.
- Dr. M. Alex O. Vasilescu (Massachusetts Institute of Technology)
Title: Hierarchical TensorFaces.
- Mr. Nadav Cohen (Hebrew University)
Title: On the Expressive Power of Convolutional Networks:
The Use of Hierarchical Tensor Decompositions for Network Analysis and Design.
- Prof. René Vidal (Johns Hopkins University)
Title: Globally Optimal Structured Low-Rank Matrix and Tensor Factorization.
- Prof. Richard Hartley (Australian National University)
- Submission Deadline:
1st of April, 2017 20th Of April, 2017 (the deadline is extended)closed
- Reviews submitted in CMT: 5th of May, 2017
- Decision to Authors: 8-10th of May, 2017
- Camera Ready: 15th of May, 2017
- TMCV Workshop: 26th of July, 2017
- The CMT site has been opened now for submission. New users can register and log in now.
- The call for papers in the pdf format can be accessed here.
- Regarding the submission guidelines, we will follow the standard CVPR rules which can be accessed here.