Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLi, Guankaien_US
dc.identifier.citationLi, G. (2021). Deep learning approaches for object co-segmentation and one-shot segmentation. Master's thesis, Nanyang Technological University, Singapore.
dc.description.abstractImage co-segmentation is an active computer vision task that aims to discover and segment the shared objects given multiple images. Recently, researchers design various learning-based algorithms to handle the co-segmentation task. The main difficulty in this task is how to effectively transfer information between images to infer the common object regions. In this thesis, we present CycleSegNet, an effective and novel approach for the co-segmentation task. Our network design has two key components: a region correspondence module which is the basic operation for exchanging information between local image regions, and a cycle refinement module which utilizes ConvLSTMs to progressively update image embeddings and exchange information in a cycle manner. Experiment results on four popular benchmark datasets --- PASCAL VOC dataset, MSRC dataset, Internet dataset, and iCoseg dataset indicate that our proposed approach greatly outperforms the existing networks and achieves new state-of-the-art performance. In addition to image co-segmentation, we also explore a method to solve one-shot segmentation with only weak supervision (bounding box). One-shot semantic segmentation has recently gained attention for its strong generalization ability to segment unseen-class images given only limited annotated image. However, existing methods in one-shot object segmentation have mainly relied on manually pixel-wise labeled segmentation masks. The main challenge in this task is limited data and weak supervision. In this thesis, we present an effective approach, which utilizes the recent weakly-supervised semantic segmentation method to generate pseudo mask labels in the bounding box regions and then integrates the detailed information and correlation between support image and query image for solving one-shot image segmentation. Extensive experiments on the PASCAL-5i dataset show that our weakly-supervised method narrows down the performance gap between bounding box supervision and pixel-wise annotations, and performs comparably with the state-of-the-art fully-supervised one-shot methods.en_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDeep learning approaches for object co-segmentation and one-shot segmentationen_US
dc.typeThesis-Master by Researchen_US
dc.contributor.supervisorLin Guoshengen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
dc.contributor.researchHardware & Embedded Systems Lab (HESL)en_US
item.fulltextWith Fulltext-
Appears in Collections:SCSE Theses
Files in This Item:
File Description SizeFormat 
Li Guankai_thesis_SCSE(MENG).pdf7.8 MBAdobe PDFView/Open

Page view(s)

Updated on Jul 3, 2022

Download(s) 50

Updated on Jul 3, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.