Advancing video segmentation by integrating advanced learning paradigms
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Abdelfattah, Islam
issued: 2024-10-03T18:44:13Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Video segmentation involves isolating specific objects or the foreground from the background in a video, for example, separating people or vehicles from the rest of the scene. This task is crucial in many applications such as video editing, surveillance, autonomous vehicles, and augmented reality. Advanced methods often utilize Deep Learning (DL) to manage the complexities of video sequences, including motion and occlusion. Despite recent progress, current state-of-the-art methods face challenges such as catastrophic forgetting, poor domain generalization, and the loss of object details when objects are occluded by other objects. This thesis addresses these critical challenges in video segmentation by proposing innovative methodologies to enhance segmentation accuracy, robustness, and domain generalization. We introduce a Knowledge Distillation Network (KDNet) and a new training technique to combat catastrophic forgetting and improve domain generalization by leveraging advanced learning paradigms such as federated learning, continual learning, knowledge distillation, and few-shot learning. This approach yields significant improvements across diverse datasets while ensuring data privacy. Additionally, we propose a Mask Enhancement Model (MEM) to mitigate the loss of object details by integrating a foundational model output and employing mask fusion techniques, resulting in better segmentation masks. Experimental results demonstrate KDNet’s ability to achieve state-of-the art domain generalization performance on multiple datasets in foreground segmentation and adapt to new unseen domains using as few as one labelled image (one-shot learning). MEM also proves effective, versatile, and robust performance, achieving state-of-the-art results in Semi-Supervised Video Object Segmentation (S-SVOS).
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89330
note: Advancing Video Segmentation byIntegrating Advanced LearningParadigmsbyIslam AbdelfattahM.Eng., University of Ottawa, 2022B.Sc., Hons., Arab Academy for Science, Technology & Maritime Transport , 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe College of Graduate Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)October 2024© Islam Abdelfattah 2024The following individuals certify that they have read, and recommend tothe College of Graduate Studies for acceptance, a thesis entitled:Advancing Video Segmentation by Integrating Advanced Learning Paradigmssubmitted by Islam Abdelfattah in partial fulfillment of the requirementsof the degree of Master of ScienceExamining Committee:Mohamed S. Shehata, The University of British Columbia, Okanagan CampusSupervisorFatemeh Fard, The University of British Columbia, Okanagan CampusSupervisory Committee MemberGema Rodríguez-Pérez, The University of British Columbia, Okanagan CampusSupervisory Committee MemberShahria Alam, The University of British Columbia, Okanagan CampusUniversity ExamineriiAbstractVideo segmentation involves isolating specific objects or the foreground fromthe background in a video, for example, separating people or vehicles fromthe rest of the scene. This task is crucial in many applications such as videoediting, surveillance, autonomous vehicles, and augmented reality. Advancedmethods often utilize Deep Learning (DL) to manage the complexities ofvideo sequences, including motion and occlusion. Despite recent progress,current state-of-the-art methods face challenges such as catastrophic forget-ting, poor domain generalization, and the loss of object details when objectsare occluded by other objects.This thesis addresses these critical challenges in video segmentation byproposing innovative methodologies to enhance segmentation accuracy, ro-bustness, and domain generalization. We introduce a Knowledge DistillationNetwork (KDNet) and a…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0445484
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Shehata, Mohamed S.
type: Text
type: Still Image
identifier URI: http://hdl.handle.net/2429/89330