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.