This tutorial aims at promoting discussions among researchers investigating innovative second-order (bilinear), kernel and tensor-based approaches to computer vision problems. Specifically, we will stimulate discussions on recent advances, ongoing developments, and novel applications of bilinear, kernel and multilinear algebra, optimization, and feature representations using matrices and tensors in the context of CNN learning.


We have addressed a wide range of theoretical and practical issues including, but not limited to the following topics:


Below is the program of the tutorial that took place on the 2nd of November, 2019. Below is the Detailed Program with abstracts and biographies of our speakers (or click on links in tables).

Afternoon Session

Time Speaker Title
13:30 Organizers Welcome
13:35 Prof. René Vidal Invited Talk I: Global Optimality in Separable Dictionary Learning
14:15 Prof. Richard Hartley Invited Talk II: Kernels on Manifolds
14:55 Dr. Piotr Koniusz Tutorial Part I. Foundations of Second- and Higher-order Representations /slides/
15:35 Coffee break Venue
16:10 Assoc. Prof. Lei Wang Tutorial Part II. Learning SPD-matrix-based Representation for Visual Recognition /slides/
16:50 Dr. Subhransu Maji Invited Talk III: Improving the Generalization and Efficiency of Second-order Representations /slides/
17:30 Prof. Ruiping Wang Tutorial Part III. Riemannian Metric Learning and its Vision Applications /slides/
18:10 Organizers Closing remarks



Below is the list of speakers (in no particular order) who gave a talk during the tutorial (including organizers):


If you wish to cite any topics raised during the tutorial, refer to specific papers of our speakers. Additionally, you are welcome to cite the tutorial itself:

  title = {Second- and Higher-order Representations in Computer Vision},
  author = {P. Koniusz and M. Harandi and L. Wang and R. Wang},
  howpublished = {ICCV Tutorial, \url{}},
  note = {Accessed: 02-11-2019},
  year = {2019},