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 takes place on the 4th of December, 2022. Below is the Detailed Program with abstracts and biographies of our speakers (or click on links in tables).

Afternoon Session

Time Speaker Title
14:00 Organizers Welcome
14:11 Dr. Naila Murray Invited Talk I: Normalization and reweighting techniques for effectively aggregating higher-order local visual representations.
15:00 A/Prof. Mehrtash Harandi Invited Talk II: Poincaré Kernels for Hyperbolic Representations.
15:51 Short break Venue
16:00 Prof. Ruiping Wang Invited Talk III: Riemannian Metric Learning and its Vision Applications.
16:51 Dr. Piotr Koniusz Invited Talk IV: Understanding High Order Pooling.
17:40 Organizers Closing remarks



Below is the list of speakers who will give 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 = {Higher-order Visual Representation Learning},
  author = {S. Rahman and S. Zhang and P. Moghadam and P. Koniusz},
  howpublished = {ACCV Tutorial, \url{}},
  note = {Accessed: 04-12-2022},
  year = {2022},