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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01sq87bx475
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dc.contributor.advisorDeng, Jia-
dc.contributor.authorLi, Matthew-
dc.date.accessioned2019-09-04T17:46:47Z-
dc.date.available2019-09-04T17:46:47Z-
dc.date.created2019-05-04-
dc.date.issued2019-09-04-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01sq87bx475-
dc.description.abstractWe propose a new network model and training strategy to improve object detection accuracy for classes with few or no localized annotated training samples. Our approach extracts ideas from related works and applies them to CornerNet, a state-of-the-art object detection system. Specifically, we modify the original CornerNet architecture to facilitate hierarchical learning for over 9000 object classes. This allows us to train jointly on the COCO detection dataset as well as the ImageNet classification dataset, in a semi-supervised fashion. First, we demonstrate that the hierarchical learning and related modifications do not significantly degrade the performance over the original COCO validation set. Finally, we validate the performance over weakly supervised classes using the ImageNet detection task, for which 156 of 200 classes do not have object detection samples in our training set.en_US
dc.format.mimetypeapplication/pdf-
dc.language.isoenen_US
dc.titleWeakly Supervised Object Detection: Hierarchical Learning with CornerNeten_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2019en_US
pu.departmentComputer Scienceen_US
pu.pdf.coverpageSeniorThesisCoverPage-
pu.contributor.authorid961095022-
pu.certificateCenter for Statistics and Machine Learningen_US
Appears in Collections:Computer Science, 1988-2021

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