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:


Below is the list of speakers who have kindly agreed (tentatively) to give talks during the workshop: