Efficient Visual Learning for Scene Understanding
Author(s)
Liu, Yen-Cheng
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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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2023-12-05
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Dissertation