event
PhD Defense by Yen-Cheng Liu
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Title: Efficient Visual Learning for Scene Understanding
Date: Tuesday, November 21, 2023
Time: 12:00 - 1:30 pm EST / 9:00 - 10:30 am PST
Location: https://gatech.zoom.us/j/7745230525
Yen-Cheng Liu
Machine Learning PhD Candidate
School of Electrical and Computer Engineering
Georgia Institute of Technology
Committee
- Dr. Zsolt Kira (Advisor), School of Interactive Computing, Georgia Tech
- Dr. Judy Hoffman, School of Interactive Computing, Georgia Tech
- Dr. Larry Heck, School of Electrical and Computer Engineering and the School of Interactive Computing, Georgia Tech
- Dr. Mark Davenport, School of Electrical and Computer Engineering, Georgia Tech
- Dr. Diyi Yang, Computer Science Department, Stanford University
Abstract
Significant advancements in scene understanding have been driven by deep neural networks. These learning-based frameworks enhance performance through extensive training datasets and a large number of trainable parameters. However, they are less scalable and require substantial computational and financial resources. This dissertation investigates two aspects of efficient visual learning for scene understanding: label-efficient learning and parameter-efficient learning. To reduce label supervision in instance-level scene understanding tasks, we develop a series of semi-supervised learning frameworks. These frameworks improve the label efficiency under various detector architectures and unconstrained data settings. To reduce parameter usage in multi-task training, we re-evaluate parameter-efficient methods from NLP for scene understanding and then propose a more parameter-efficient method for vision architectures. These advancements demonstrate the practicality and adaptability of efficient learning frameworks in diverse, resource-constrained environments.
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Status
- Workflow Status:Published
- Created By:Tatianna Richardson
- Created:11/13/2023
- Modified By:Tatianna Richardson
- Modified:11/13/2023
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