Modern deep learning in computer vision is primarily driven by networks designed to work on Euclidean manifolds. But is Euclidean geometry truly the best fit for these models, or is it simply the most convenient choice? Recent breakthroughs in machine learning and computer vision suggest that hyperbolic geometry could be a compelling alternative. This geometry offers unique advantages for embedding complex structures such as hierarchies, graphs, text, images, and videos.
In response to these developments, this tutorial introduces hyperbolic geometry as a promising tool for advancing in medical imaging. We aim to make this topic accessible to both researchers and practitioners. Whether you're new to the concept or looking to deepen your expertise, this tutorial offers both theoretical insights and practical guidance.
What to Expect
At the conference, we’ll provide an accessible introduction to hyperbolic geometry, designed especially for non-mathematicians. Our goal is to focus on intuition and high-level concepts, ensuring that the topic is approachable and engaging for everyone.
We will also cover the latest advancements in the field, exploring the current use of hyperbolic geometry in computer vision and medical imaging from both supervised and unsupervised perspectives. By the end of the session, we’ll highlight open research challenges and the future potential of hyperbolic geometry for visual understanding.
Practical Insights and Hands-On Experience
Unlike typical theoretical tutorials, we go beyond the foundations. Our tutorial website will feature interactive, notebook-style code snippets based on foundational works in hyperbolic geometry. These resources are designed to help you gain hands-on experience, lowering the barrier to entry and allowing you to actively explore this exciting new research area.
Whether you're just starting your journey or are already familiar with deep learning, this tutorial is your guide to understanding and utilizing hyperbolic geometry in computer vision.
