Cortical Column Networks: Learning object identity and pose representations from pixel observations
Channel: Numenta
Date: 30/09/2021

Drawing inspirations from the Thousand Brains Theory on Intelligence, guest speakers Tim Verbelen and Toon Van de Maele from Ghent University share their recent work on learning object identity and pose representations from pixel observations. 

0:00 Introduction
3:42 Active Inference 
18:40 Visual Foraging
26:40 Cortical Column Networks
44:28 Q&A 

➤ Paper - https://arxiv.org/abs/2108.11762
➤ Blog post - https://thesmartrobot.github.io/2021/08/26/thousand-brains.html
➤ For more information on The Smart Robot: https://thesmartrobot.github.io/

Abstract
Although modern object detection and classification models achieve high accuracy, these are typically constrained in advance on a fixed train set and are therefore not flexible enough to deal with novel, unseen object categories. Moreover, these models most often operate on a single frame, which may yield incorrect classifications in case of ambiguous viewpoints. In this paper, we propose an active inference agent that actively gathers evidence for object classifications, and can learn novel object categories over time. Drawing inspiration from the Thousand Brains Theory of Intelligence, we build object-centric generative models composed of two information streams, a what- and a where-stream. The what-stream predicts whether the observed object belongs to a specific category, while the where-stream is responsible for representing the object in its internal 3D reference frame. In this talk, we will present our models and some initial results both in simulation and on a real-world robot.

Bio
Tim Verbelen received his M.Sc. and Ph.D. degrees in Computer Science Engineering at Ghent University in 2009 and 2013 respectively. Since then, he has been working as a senior researcher for Ghent University and imec. His main research interests include perception and control for autonomous systems using deep learning techniques and high-dimensional sensors such as camera, lidar and radar. In particular, he is active in the domain of representation learning and reinforcement learning, inspired by cognitive neuroscience theories such as active inference.
 
Toon Van de Maele received his M.Sc. degree in Computer Science Engineering at Ghent University in June 2019. Since then, he has been working on a Ph.D. degree on learning representations for 3D scenes at Ghent University. His main interest lies in the combination of deep learning approaches for robotic perception, using biologically-inspired techniques.
- - - - - 
Numenta is leading the new era of machine intelligence. Our deep experience in theoretical neuroscience research has led to tremendous discoveries on how the brain works. We have developed a framework called the Thousand Brains Theory of Intelligence that will be fundamental to advancing the state of artificial intelligence and machine learning. By applying this theory to existing deep learning systems, we are addressing today’s bottlenecks while enabling tomorrow’s applications. 

Subscribe to our News Digest for the latest news about neuroscience and artificial intelligence:
https://tinyurl.com/NumentaNewsDigest

Subscribe to our Newsletter for the latest Numenta updates:
https://tinyurl.com/NumentaNewsletter

Our Social Media: 
https://twitter.com/Numenta
https://www.facebook.com/OfficialNumenta
https://www.linkedin.com/company/numenta

Our Open Source Resources:
https://github.com/numenta
https://discourse.numenta.org/

Our Website: 
https://numenta.com/