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ICVSS Computer Vision: a Renaissance


SpeakersSyllabusTitles & Abstracts
Michael Bronstein
Twitter Inc. & University of Oxford, UK
Geometric Deep Learning Geometric Deep Learning - The Erlangen Programme of ML
Dima Damen
University of Bristol, UK
Video Understanding, Fine-Grained, Multi-Modal, Egocentric Video Understanding - an Egocentric Perspective
David Forsyth
University of Illinois, USA
Fake Data, Mathematical Structure, Intrinsic Images, Scene Relightning Learning shading and lighting without ground truth
Abhishek Gupta
Massachusetts Institute of Technology, USA
Deep Reinforcement Learning, Robotics How to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World
Matthew Johnson
Microsoft Research, Cambridge, UK
Fourier Feature Networks, Neural Rendering, Volumetric Ray Casting, Azure Machine Learning Fourier Feature Networks and Neural Volume Rendering
Laura Leal-Taixé
Technische Universität München, DE
Multi-Object Tracking (MOT), Tracking-by-Regression, Tracking-by-Attention Shifting paradigms in multi-object tracking
Hao Li
Pinscreen, Inc. & MBZUAI, UAE
Neural Rendering, Dialogue Replacement Don't get lost in Translation: The Neural Rendering of the Champion
Matthias Niessner
Technische Universität München, DE
Neural Rendering, 3D Reconstruction, Differentiable Optimization Neural Rendering meets 3D Reconstruction
Gerhard Reitmayr
Qualcomm, US
Augmented Reality, Virtual Reality Trends and Challenges in Building XR devices
Elisa Ricci
University of Trento, IT
Learning to adapt: paradigms and challenges in adapting deep models to domain and semantic shift Learning to adapt: paradigms and challenges in adapting deep models to domain and semantic shift
Davide Scaramuzza
ETH Zürich, CH
Autonomous Robots, Perception and Control Learning Robot Navigation: from Perception to Action
Jamie Shotton
Wayve, UK
Autonomous Vehicles, Embodied AI The Next Frontier in Embodied AI: Autonomous Vehicles
Vincent Sitzmann
Massachusetts Institute of Technology, USA
3D vision, Neural Rendering, Visual Representations, Self-Supervised Learning Learning to Perceive the 3D World from 2D Images
Andrea Vedaldi
University of Oxford, UK
Unsupervised Learning, Contrastive Learning, 3D Geometry Meaningful unsupervised learning: from labelling to 3D geometry


SpeakersSyllabusRules of Engagement
Stefano Soatto
Amazon and University of California Los Angeles, USA
Reading Group Competition Sent by email


SpeakersSyllabusRules of Engagement
Fabio Galasso
Sapienza University of Rome, Italy
Essay Competition Sent by email


Industrial Panel

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