Deep learning for visual understanding

Leiden Repository

Deep learning for visual understanding

Type: Doctoral Thesis
Title: Deep learning for visual understanding
Author: Guo, Y.
Issue Date: 2017-10-05
Keywords: Deep learning
Computer Vision
Image Classification
Image Captioning
Abstract: With the dramatic growth of the image data on the web, there is an increasing demand of the algorithms capable of understanding the visual information automatically. Deep learning, served as one of the most significant breakthroughs, has brought revolutionary success in diverse visual applications, including image classification, object detection, image segmentation, image captioning and etc. The purpose of this thesis is to explore and design new deep learning algorithms for better visual understanding. The main purpose of the thesis is to develop new algorithms which can improve the understanding of images. To fulfill this, it focuses on two visual applications: image classification and image captioning. Image classification aims to classify images into pre-defined categories, and helps people to know what objects the images contain. Image captioning attempts to generate a sentence to describe the images. In addition to the object, the generated sentence should also contain the action, relation and etc. 
Promotor: Supervisor: Kok J.N. Co-Supervisor: Lew M.S.
Faculty: Science
University: Leiden
Uri: urn:isbn:97789462997295
Handle: http://hdl.handle.net/1887/52990
 

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application/pdf Bibliography 224.2Kb View/Open
application/pdf Summary in English 146.3Kb View/Open
application/pdf Summary in Dutch 146.8Kb View/Open
application/pdf Acknowledgements_Curriculum Vitae 147.2Kb View/Open
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