Uploaded April 2024 | Updated September 2026, 2 weeks ago
https://www.ri.cmu.edu/event/toward-an-imagenet-moment-for-synthetic-data/
Jia Deng
Associate Professor
Department of Computer Science, Princeton University
April 5, 2024
Toward an ImageNet Moment for Synthetic Data
Abstract:
Data, especially large-scale labeled data, has been a critical driver of progress in computer vision. However, many important tasks remain starved of high-quality data. Synthetic data from computer graphics is a promising solution to this challenge, but still remains in limited use. This talk will present our work on Infinigen, a procedural synthetic data generator designed to create unlimited high-quality labelled data for computer vision. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules. I will present our initial system focused on natural objects, and our ongoing work, which expands coverage to indoor environments.
Bio:
Jia Deng is an Associate Professor of Computer Science at Princeton University. His research focuses on computer vision and machine learning. He received his Ph.D. from Princeton University and his B.Eng. from Tsinghua University, both in computer science. He is a recipient of the Sloan Research Fellowship, the NSF CAREER award, and the ONR Young Investigator award.
https://www.ri.cmu.edu/event/toward-an-imagenet-moment-for-synthetic-data/
Jia Deng
Associate Professor
Department of Computer Science, Princeton University
April 5, 2024
Toward an ImageNet Moment for Synthetic Data
Abstract:
Data, especially large-scale labeled data, has been a critical driver of progress in computer vision. However, many important tasks remain starved of high-quality data. Synthetic data from computer graphics is a promising solution to this challenge, but still remains in limited use. This talk will present our work on Infinigen, a procedural synthetic data generator designed to create unlimited high-quality labelled data for computer vision. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules. I will present our initial system focused on natural objects, and our ongoing work, which expands coverage to indoor environments.
Bio:
Jia Deng is an Associate Professor of Computer Science at Princeton University. His research focuses on computer vision and machine learning. He received his Ph.D. from Princeton University and his B.Eng. from Tsinghua University, both in computer science. He is a recipient of the Sloan Research Fellowship, the NSF CAREER award, and the ONR Young Investigator award.
