Active Learning for Object Detection

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dc.contributor.advisorIyer, Rishabh Krishnan
dc.contributor.committeeMemberGogate, Vibhav
dc.contributor.committeeMemberXiang, Yu
dc.creatorGhosh, Saikat
dc.date.accessioned2023-08-30T22:08:48Z
dc.date.available2023-08-30T22:08:48Z
dc.date.created2022-05
dc.date.issued2022-05-01T05:00:00.000Z
dc.date.submittedMay 2022
dc.date.updated2023-08-30T22:08:49Z
dc.description.abstractIn this thesis, we explore the interesting paradigm of Active Learning which is garnering a lot of attention in recent years within the Machine Learning community, and introduce approaches to how we can leverage Active Learning strategies to acquire significant performance gain on a niche task like Object Detection. From an application perspective, we broadly focus on two aspects - 1. Apply Active Learning strategies on the standard Object Detection task 2. Introduce targeted Active Learning for Object detection that improves performance on rare class/rare slices of data We will begin with a brief introduction to standard object detection mechanisms and various Active Learning strategies available and then we will dive into the experimental setup for implementing standard Active Learning and targeted Active Learning for Object Detection, discuss the empirical results obtained from different experimental settings and finally conclude with key observations from the experiments.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/9825
dc.language.isoen
dc.subjectComputer Science
dc.titleActive Learning for Object Detection
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeSchool of Engineering and Computer Science
thesis.degree.departmentComputer Science
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.nameMSCS

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