Continual foreground segmentation using limited data
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: Osman, Islam
issued: 2023-05-01T20:12:35Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Current state-of-the-art foreground segmentation models have achieved great success when trained with massive labeled datasets. However, these models have limitations including 1) they can not capture the fine details of the exterior silhouette of objects. 2) The performance of these models degrades when trained with limited data. 3) The performance of these models is limited in the scope of the problems they were trained on. When those models are trained using a broader scope of problems (i.e., multiple datasets), their performance degrade due to the catastrophic forgetting. This thesis proposes novel deep-learning models that overcome the problems mentioned above. For the first problem, deep-learning models are proposed: MODY-Net, REFNet, and TransBlast. The results show that REFNet solved the problem of missing fine details and achieved state-of-the-art performance. The f-measure performance of REFNet is higher than other foreground segmentation models by approximately 4%. To overcome the second problem of learning with limited data, three novel few-shot learning techniques KTNet, ORIF-TR, and FeSh-Net, are proposed. KTNet combines few-shot and self-supervised learning in both in-domain and out-of-domain image classification. Then, ORIF-TR and FeSh-Net reformulated the few-shot to foreground segmentation. FeSh-Net uses few-shot learning to learn from an exemplar frame to segment the current frame, boosting the current state-of-the-art performance by 7.7%. To solve the last problem (catastrophic forgetting), three novel lifelong-learning techniques with server-client architecture are proposed: TBPI, KDNet, and DGT. These learning techniques allow a large model (server) to learn from continuous data without forgetting. In contrast, during inference time, a small model (client) is instantiated from the larger model (server) to segment foreground objects with high performance and low inference time. DGT performance is higher than state-of-the-art (REFNet) by 4.65% an…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84528
note: Continual Foreground Segmentation with Limited DatabyIslam OsmanM.Sc., Ain Shams University, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe College of Graduate StudiesComputer ScienceTHE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)April 2023© Islam Osman, 2023The following individuals certify that they have read, and recommend to the College of Graduate Studiesfor acceptance, a thesis/dissertation entitled:Continual Foreground Segmentation with Limited Datasubmitted by Islam Osman in partial fulfillment of the requirements ofthe degree of Doctor of Philosophy .Mohamed S. ShehataSupervisorRamon LawrenceSupervisory Committee MemberKhalad HasanSupervisory Committee MemberSuliman GargoumUniversity ExaminerYang WangExternal ExamineriiAbstractCurrent state-of-the-art foreground segmentation models have achieved great success when trained withmassive labeled datasets. However, these models have limitations including 1) they can not capture thefine details of the exterior silhouette of objects. 2) The performance of these models degrades when trainedwith limited data. 3) The performance of these models is limited in the scope of the problems they weretrained on. When those models are trained using a broader scope of problems (i.e., multiple datasets), theirperformance degrade due to the catastrophic forgetting.This thesis proposes novel deep-learning models that overcome the problems mentioned above. For thefirst problem, deep-learning models are proposed: MODY-Net, REFNet, and TransBlast. The results showthat REFNet solved the problem of missing fine details and achieved state-of-the-art performance. Thef-measure performance of REFNet is higher than other foreground segmentation models by approximately4%.To overcome the second problem of learning with limited data, three novel few-shot learning techniquesKTNet, ORIF-TR, and FeSh-Net, are proposed. KTNet combines few-shot and self-supervised learningin both in-domain …
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0431525
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
identifier URI: http://hdl.handle.net/2429/84528