Conference paper Open Access

ONLINE MULTI-TASK LEARNING FOR SEMANTIC CONCEPT DETECTION IN VIDEO

Foteini Markatopoulou; Vasileios Mezaris; Ioannis Patras


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  <identifier identifierType="URL">https://zenodo.org/record/159241</identifier>
  <creators>
    <creator>
      <creatorName>Foteini Markatopoulou</creatorName>
      <affiliation>Information Technologies Institute (ITI), CERTH, Queen Mary University of London</affiliation>
    </creator>
    <creator>
      <creatorName>Vasileios Mezaris</creatorName>
      <affiliation>Information Technologies Institute (ITI), CERTH</affiliation>
    </creator>
    <creator>
      <creatorName>Ioannis Patras</creatorName>
      <affiliation>Queen Mary University of London</affiliation>
    </creator>
  </creators>
  <titles>
    <title>ONLINE MULTI-TASK LEARNING FOR SEMANTIC CONCEPT DETECTION IN VIDEO</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2016</publicationYear>
  <subjects>
    <subject>Concept detection</subject>
    <subject>Multi-task learning</subject>
    <subject>Video</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2016-09-25</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/159241</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/ICIP.2016.7532344</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ecfunded</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/invid-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In this paper we propose an online multi-task learning algorithm for video concept detection. In particular, we extend the Efficient Lifelong Learning Algorithm (ELLA) in the following ways: a) we solve the objective function of ELLA using quadratic programming instead of solving the Lasso problem, b) we add a new label-based constraint that considers concept correlations, c) we use linear SVMs as base learners instead of logistic regression. Experimental results show improvement over both the single-task learning methods typically used in this problem and the original ELLA algorithm.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687786/">687786</awardNumber>
      <awardTitle>In Video Veritas – Verification of Social Media Video Content for the News Industry</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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