Deep Zero- and Few-shot Learning in Computer Vision
type: CreativeWork
description: Most CNN models rely on the large-scale annotated training data, and the performance turns to be low when the size of training data is limited, therefore numbers of large-scale datasets are proposed for vision tasks. Though training on large-scale dataset can significantly improve the performance, creating such datasets for novel scenarios is costly. Moreover, relying on the number of samples is not consistent with human's learning abilities, as humans have the ability to understand novel concepts from limited examples. Transfer learning is a machine learning topic that addresses the above limitations in current deep models, and it studies how to make machines exploit the acquired knowledge to solve new problems, thus learning by generalizing from existing to new concepts, a trait akin to humans. Empirically, transfer learning for computer vision problems includes the following subtopics: i) zero-shot learning, ii) few-shot learning, and iii) domain adaptation. In this thesis, we study zero-shot and few-shot learning to demonstrate how to learn better from limited training samples. Zero-shot learning requires learning a mapping that associates feature vectors extracted from images with semantic annotations that describe objects and/or scenes of interest. Our first work addresses the weakness of linear mapping in previous zero-shot learning models by learning via non-linear kernelized projections between features and attribute spaces to promote accuracy. We propose an easy learning objective inspired by the Linear Discriminant Analysis method with an incoherence mechanism. The dominant challenge in the generalized zero-shot setting is the unbalanced data distribution during training and testing, which makes it difficult for the classifier to distinguish if a testing sample is from a seen or unseen class. Inspired by GAN-based models, we propose the model selection mechanism, in which we leverage two sources of datapoints to train the model selection classifier to rec…
abstract: Most CNN models rely on the large-scale annotated training data, and the performance turns to be low when the size of training data is limited, therefore numbers of large-scale datasets are proposed for vision tasks. Though training on large-scale dataset can significantly improve the performance, creating such datasets for novel scenarios is costly. Moreover, relying on the number of samples is not consistent with human's learning abilities, as humans have the ability to understand novel concepts from limited examples. Transfer learning is a machine learning topic that addresses the above limitations in current deep models, and it studies how to make machines exploit the acquired knowledge to solve new problems, thus learning by generalizing from existing to new concepts, a trait akin to humans. Empirically, transfer learning for computer vision problems includes the following subtopics: i) zero-shot learning, ii) few-shot learning, and iii) domain adaptation. In this thesis, we study zero-shot and few-shot learning to demonstrate how to learn better from limited training samples. Zero-shot learning requires learning a mapping that associates feature vectors extracted from images with semantic annotations that describe objects and/or scenes of interest. Our first work addresses the weakness of linear mapping in previous zero-shot learning models by learning via non-linear kernelized projections between features and attribute spaces to promote accuracy. We propose an easy learning objective inspired by the Linear Discriminant Analysis method with an incoherence mechanism. The dominant challenge in the generalized zero-shot setting is the unbalanced data distribution during training and testing, which makes it difficult for the classifier to distinguish if a testing sample is from a seen or unseen class. Inspired by GAN-based models, we propose the model selection mechanism, in which we leverage two sources of datapoints to train the model selection classifier to rec…
author: Zhang, Hongguang
language: en_AU
date published: 2020
date modified: 2026-07-24T01:07:27Z
provider: Australian Catholic University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Australian Catholic University

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1885/207317