Matrix and tensor factorizations have a long history in machine learning. Being able to recover latent factors in the data and flexible enough to accommodate a large set of constraints and regularizations, matrix and tensor factorization methods have found several applications in computer vision problems, providing a natural framework to handle the inherently complex structure of visual data (e.g., spatial and temporal dimensions in videos) and their multi-aspect, multimodal, and heterogeneous nature (e.g., RGB and depth images of the same object). Besides that, recent renaissance of tensor methods is furthermore attributed to current advances on the development of scalable algorithms for tensor operations and novel models through tensor representations that have deemed successful in unsupervised learning of latent variable models and dictionaries, uncovering high-order relations in the data, training deep neural networks, and explaining some of their theoretical aspects.

These progresses, along with industry solutions such as Google TensorFlow, Torch, and Tensor Processing Unit, trigger new directions and problems towards matrix and tensor methods in computer vision. The workshop aims to foster discussion, discovery, and dissemination of research activities and outcomes in this area and encourages breakthroughs. We will bring together researchers in theories and applications who are interested in analysis and factorization of tensors and matrices as second-order tensors and development of tensor-based algorithms for computer vision tasks. We will also invite researchers from related areas, such as numerical linear algebra, high-performance computing, deep learning, data analysis, among others, to contribute to this workshop. We believe that this workshop can foster new directions, closer collaborations and novel applications. We also expect a deeper conversation regarding why learning with tensors at current stage is important, where it is useful, what tensor computation software and hardware work well in practice and, how we can progress further with interesting research directions and open problems.



Call for papers

We encourage discussions on recent advances in theory, algorithms, and applications of matrix and tensor factorization in computer vision. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

 Advances in matrix and tensor factorization methods:

  • Matrix and tensor factorization methods for component analysis, dictionary learning, and latent variable models.
  • Matrix and tensor models with structural constraints (e.g., sparsity, low-rank, non-negativity etc.)
  • Matrix and tensor methods on non-Euclidean domains (e.g., kernels, manifolds, graphs etc.)
  • Robust to noise and outliers matrix and tensor factorizations
  • Mathematical optimization methods for matrix and tensor factorizations

Applications of matrix and tensor factorization methods to computer vision problems:

  • Factorization methods for rigid and non-rigid structure from motion, photometric stereo, and cameral calibration
  • Spatial and temporal segmentation and clustering of videos and image ensembles
  • Action and behavior analysis using tensors and tensor decompositions
  • Image enhancement, de-noising, and impainting using tensor methods
  • Tensor methods in medical imaging
  • Feature extraction using tensor methods
  • Tensor factorization for fusion of visual information with text, audio, and other modalities
  • Fast and scalable implementations of tensor methods for computer vision tasks
  • Software and hardware for tensors

Open and emerging research questions:

  • To role of invariance in learning with matrices and tensor. How to design matrix and tensor factorization that capture desirable invariances (e.g., invariant to geometric transformation, spatial and temporal deformations etc)?
  • Deep and non-linear matrix and tensor factorizations
  • Algorithms with theoretical guarantees for factorization methods

Important dates

Paper submission deadline: 1 August 2017
Author notification: 18 August 2017
Camera ready: 25 August 2017
Workshop: 23 October 2017


All submissions will be handled electronically via the conference’s CMT Website (
The format for paper submission is the same as the ICCV main conference. The review will be double-blind. Each submission will be reviewed by at least two 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 10 pages (excluding references) will be rejected without review.

Invited speakers




Yannis Panagakis is a Lecturer  (USA equivalent to Assistant Professor) in Computer Science at Middlesex University London and a Research Fellow at the Department of Computing, Imperial College London. He received his PhD and MSc degrees from the Department of Informatics, Aristotle University of Thessaloniki and his B.Sc. degree in Informatics and Telecommunication from the University of Athens, Greece. Yannis received various scholarships and awards for his studies and research, including the prestigious Marie-Curie Fellowship in 2013. He currently serves as an Associate Editor of Image and Vision Computing Journal. His current research interests include machine learning, signal processing, and mathematical optimization with applications to computer vision, human behavior analysis, and music information research. His work has been featured in top venues in the field, such as IEEE T-PAMI, TIP, IJCV as well as CVPR, and ICCV. Yannis will be the Workshops Chair in BMVC 2017.


Stefanos Zafeiriou is currently a Senior Lecturer (USA equivalent to Associate Professor) in Pattern Recognition/Statistical Machine Learning for Computer Vision with the Department of Computing, Imperial College London, London, U.K, and a Distinguishing Research Fellow with University of Oulu under Finish Distinguishing Professor Program. He was a recipient of the Prestigious Junior Research Fellowships from Imperial College London in 2011 to start his own independent research group. He was the recipient of the President's Medal for Excellence in Research Supervision for 2016. He has received various awards during his doctoral and post-doctoral studies. He currently serves as an Associate Editor of the IEEE Transactions on Cybernetics the Image and Vision Computing Journal. He has been a Guest Editor of over six journal special issues and co-organised over nine workshops/special sessions on face analysis topics in top venues, such as CVPR/FG/ICCV/ECCV (including two very successfully challenges run in ICCV’13 and ICCV’15 on facial landmark localisation/tracking). He has co-authored over 50 journal papers mainly on novel statistical machine learning methodologies applied to computer vision problems, such as 2-D/3- D face analysis, deformable object fitting and tracking, shape from shading, and human behaviour analysis, published in the most prestigious journals in his field of research, such as the IEEE T-PAMI, the International Journal of Computer Vision, the IEEE T-IP, the IEEE T-NNLS, the IEEE T-VCG, and the IEEE T-IFS, and many papers in top conferences, such as CVPR, ICCV, ECCV, ICML. His students are frequent recipients of very prestigious and highly competitive fellowships, such as the Google Fellowship, the Intel Fellowship, and the Qualcomm Fellowship. He is the General Chair of BMVC 2017.


Anima Anandkumar is currently a principal scientist at Amazon Web Services. She will be joining Caltech CMS department as a Bren endowed chair this summer.   Her research interests are in the areas of large-scale machine learning, non-convex optimization and high- dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms.  She is the recipient of several awards such as the Alfred. P. Sloan Fellowship, Microsoft Faculty Fellowship, Google research award, ARO and AFOSR Young Investigator Awards, NSF Career Award, Early Career Excellence in Research Award at UCI, Best Thesis Award from the ACM Sigmetrics society, IBM Fran Allen PhD fellowship, and several best paper awards. She has been featured in a number of forums such as the Quora ML session, Huffington post, Forbes, O’Reilly media, and so on.  She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010,  an assistant professor at U.C. Irvine between 2010 and 2016, and a visiting researcher  at Microsoft Research New England in 2012 and 2014.


Jean Kossaifi is a Research Assistant and PhD student within the Department of Computing, Imperial College London, working as part of the iBUG group under Professor Maja Pantic's supervision. His current position follows the completion of an MSc in Advanced Computing, obtained with Distinction from Imperial College London, UK. In addition, Jean also holds a French Engineering Diploma/MSc in applied mathematics, finance and computing, obtained in parallel with a BSc in Advanced Mathematics.
His research interests are primarily focused on the areas of machine learning/tensor-learning, computer vision and pattern recognition, with applications in human-computer interaction, automatic non-verbal behaviour analysis and emotion recognition.