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dc.contributor.authorPemula, Lathaen_US
dc.date.accessioned2016-10-25T08:00:37Z
dc.date.available2016-10-25T08:00:37Z
dc.date.issued2016-10-24en_US
dc.identifier.othervt_gsexam:9048en_US
dc.identifier.urihttp://hdl.handle.net/10919/73321
dc.description.abstractMany real world datasets are characterized by having a long tailed distribution, with several samples for some classes and only a few samples for other classes. While many Deep Learning based solutions exist for object recognition when hundreds of samples are available, there are not many solutions for the case when there are only a few samples available per class. Recognition in the regime where the number of training samples available for each class are low, ranging from 1 to couple of tens of examples is called Lowshot Recognition. In this work, we attempt to solve this problem. Our framework is similar to [1]. We use a related dataset with sufficient number (a couple of hundred) of samples per class to learn representations using a Convolutional Neural Network (CNN). This CNN is used to extract features of the lowshot samples and learn a classifier . During representation learning, we enforce the learnt representations to obey certain property by using a custom loss function. We believe that when the lowshot sample obey this property the classification step becomes easier. We show that the proposed solution performs better than the softmax classifier by a good margin.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectVisual Recognitionen_US
dc.subjectObject Recognitionen_US
dc.subjectComputer Visionen_US
dc.subjectLow-shot Learningen_US
dc.titleLow-shot Visual Recognitionen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Engineeringen_US
dc.contributor.committeechairBatra, Dhruven_US
dc.contributor.committeememberParikh, Devien_US
dc.contributor.committeememberAbbott, Amos L.en_US


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