Title:
Shape-biased representations for object category recognition
Shape-biased representations for object category recognition
Author(s)
Stojanov, Stefan
Advisor(s)
Rehg, James M.
Hoffman, Judy
Hoffman, Judy
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Abstract
While object recognition is a classical problem in computer vision that has witnessed incredible progress as a result of contemporary deep learning research, the key challenges of developing systems that can learn object categories from continually arriving data, from a few samples, and with limited supervision still remain. In this dissertation, we aim to borrow the learning strategy of shape bias and environmental bias of repetition, both of which are observed in young children, and apply them to continual, low-shot, and self-supervised learning of objects and object parts. In the continual learning domain, we demonstrate that repetition of learned concepts significantly ameliorates catastrophic forgetting. For low-shot learning we develop two methods for learning shape-biased object representations with decreasing supervision requirements: based on learning a joint image and 3D shape metric space from point clouds, and by self-supervised learning of object parts from multi-view pixel correspondences. We demonstrate that these methods of introducing a shape bias improve low-shot category recognition. Last, we find that contrastive learning from multi-view images allows for category-level part matching with performance competitive with baselines that have over 10 times more parameters, while being trained only on synthetic data. To support our investigations, we present two synthetic 3D object datasets, Toys200 and Toys4K, and develop a series of highly realistic synthetic data rendering systems that enable real-world generalization.
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Date Issued
2023-11-28
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Text
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Dissertation