Matthew Kowal

Matthew Kowal

PhD Student - Explaining Deep Vision Models

About Me

I am a NSERC CGS-D scholarship funded PhD student at York University supervised by Dr. Konstantinos Derpanis. My research interest is in explainable computer vision with a focus on video understanding tasks. I am currently completing a research internship at Toyota Research Institute as a member of the machine learning team, and a faculty affiliate researcher at the Vector Institute. I was previously the Lead Scientist in Residence at NextAI from 2020-2022.

Before my PhD studies began, I completed a Bachelors of Applied Science (B.A.Sc) in Applied Mathematics and Engineering with a specialization in Mechanical Engineering at Queen’s University in Kingston, Ontario, Canada. After graduating from my Bachelors degree, I worked at Morrison Hershfield as a mechanical design engineer. I designed buildings, labratories, and condos, with a team of other mechanical, evironmental, electrical, and control engineers. I then obtained my M.Sc at the Ryerson Vision Lab in August 2020 under the co-supervision of Dr. Neil Bruce and Dr. Kosta Derpanis. My M.Sc thesis focused on multi-modal action recognition.

My hobbies include health and fitness, competitive Super Smash Bros. Melee, birds, close up magic, and progressive house music.

News

  • I have been awarded the NSERC CGS-D Scholarship with a total value of $105,000! (Accepted)
  • I have accepted an offer to do a research internship at Toyota Research Institute for the Summer of 2023 at the Palo Alto HQ office!
  • I gave a talk at Vector’s Endless Summer School program on Current Trends in Computer Vision and a CVPR 2022 Recap
  • Paper accepted to the International Journal of Computer Vision (IJCV) - SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness. Paper.
  • New Preprint available on Arxiv - Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks. Paper
  • I presented a spolight presentation at the Explainable AI for Computer Vision Workshop at CVPR 2022. You can watch the recorded talk here.
  • Paper Accpted to CVPR 2022 - A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information. Paper and Project Page.
  • Paper Accepted to ICCV 2021 - Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs. Paper.
  • Paper Accepted to BMVC 2021 - Simpler Does It: Generating Semantic Labels with Objectness Guidance. Paper.
  • Paper Accepted to ICLR 2021 - Shape or Texture: Understanding Discriminative Features in CNNs. Paper.
  • Paper Accepted as an Oral to BMVC 2020 - Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness. Paper.