Zero shot hashing

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dc.contributor.author Raman, Shanmuganathan
dc.contributor.author Pachori, Shubham
dc.date.accessioned 2016-11-02T09:04:41Z
dc.date.available 2016-11-02T09:04:41Z
dc.date.issued 2016-10
dc.identifier.citation Pachori, Shubham and Raman, Shanmuganathan, “Zero shot hashing”, arXiv, Cornell University Library, DOI: arXiv:1610.02651, Oct. 2016. en_US
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/2492
dc.identifier.uri arXiv:1610.02651
dc.description.abstract This paper provides a framework to hash images containing instances of unknown object classes. In many object recognition problems, we might have access to huge amount of data. It may so happen that even this huge data doesn't cover the objects belonging to classes that we see in our day to day life. Zero shot learning exploits auxiliary information (also called as signatures) in order to predict the labels corresponding to unknown classes. In this work, we attempt to generate the hash codes for images belonging to unseen classes, information of which is available only through the textual corpus. We formulate this as an unsupervised hashing formulation as the exact labels are not available for the instances of unseen classes. We show that the proposed solution is able to generate hash codes which can predict labels corresponding to unseen classes with appreciably good precision. en_US
dc.description.statementofresponsibility by Shubham Pachori and Shanmuganathan Raman
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.subject Computer Vision en_US
dc.subject Pattern Recognition en_US
dc.title Zero shot hashing en_US
dc.type Preprint en_US


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