Graph Machine Learning for Visual Computing

CVPR 2022 Tutorial

Time:   June 20th

Hybrid Event

New Orleans, Louisiana, USA

 Zoom Link

Graph Machine Learning for Visual Computing Tutorial @ CVPR 2022

Advances in convolutional neural networks and recurrent neural networks have led to significant improvements in learning on regular grid data domains such as images and texts. However, many real-world datasets or underlying relations do not lie in such simple grid structures. Such data is irregular or non-Euclidean in structure and has complex relational information. Graph Machine Learning, especially Graph Neural Networks (GNNs), provides a potential for processing such irregular data and being capable of modeling the relation between entities, which is leading the machine learning field to a new era. Numerous data formats in the visual computing area such as point clouds, 3D meshes, scene graphs, etc. have such complex structures, which makes it challenging to model their representation for machine learning tasks. Graph Machine Learning presents powerful tools to approach the representation learning problem. Recent works have successfully applied Graph Machine Learning to handle various problems in the visual computing area such as geometric processing, scene graph generation, video understanding, multi-object relational mining, physical reasoning from vision, graphics simulation, visual navigation and so on. To spark more interests and creative thoughts, we believe it is important and beneficial to further spread the knowledge of Graph Machine Learning to the computer vision community. To this end, we organize a half-day tutorial focusing on Graph Machine Learning for Visual Computing. The tutorial would cover a wide variety of topics that involve the core theory of graph machine learning, its applications in visual computing, and the introduction to one of the most popular graph ML programming frameworks.


Time Session Speakers
{{tableData[currentCountry][0]}} Opening Remarks Organizers
{{tableData[currentCountry][1]}} Geometric Deep Learning Petar Veličković
{{tableData[currentCountry][2]}} Building GNNs with Pytorch Geometric Matthias Fey
{{tableData[currentCountry][3]}} Graph ML for Video Understanding Bernard Ghanem
{{tableData[currentCountry][4]}} 20 Minutes Break
{{tableData[currentCountry][5]}} Scene Graphs in 3D Vision Federico Tombari& Fabian Manhardt
{{tableData[currentCountry][6]}} Physion: Evaluating physical prediction from vision in humans and machines Judith Fan
{{tableData[currentCountry][7]}} Hierarchical 3D Scene Understanding for Robotics: Real-time Algorithms and Performance Guarantees Luca Carlone & Rajat Talak
CVPR 2022 Tutorial ©2022