Conference paper Open Access

Concept Language Models and Event-based Concept Number Selection for Zero-example Event Detection

Galanopoulos, Damianos; Markatopoulou, Foteini; Mezaris, Vasileios; Patras, Ioannis


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  <identifier identifierType="URL">https://zenodo.org/record/809680</identifier>
  <creators>
    <creator>
      <creatorName>Galanopoulos, Damianos</creatorName>
      <givenName>Damianos</givenName>
      <familyName>Galanopoulos</familyName>
      <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation>
    </creator>
    <creator>
      <creatorName>Markatopoulou, Foteini</creatorName>
      <givenName>Foteini</givenName>
      <familyName>Markatopoulou</familyName>
      <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation>
    </creator>
    <creator>
      <creatorName>Mezaris, Vasileios</creatorName>
      <givenName>Vasileios</givenName>
      <familyName>Mezaris</familyName>
      <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation>
    </creator>
    <creator>
      <creatorName>Patras, Ioannis</creatorName>
      <givenName>Ioannis</givenName>
      <familyName>Patras</familyName>
      <affiliation>Queen Mary University of London</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Concept Language Models and Event-based Concept Number Selection for Zero-example Event Detection</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Zero-example multimedia event detection</subject>
    <subject>Video search</subject>
    <subject>Query representation</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-06-08</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/809680</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3078971.3079043</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/invid-h2020</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-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;Zero-example event detection is a problem where, given an event query as input but no example videos for training a detector, the system retrieves the most closely related videos. In this paper we present a fully-automatic zero-example event detection method that is based on translating the event description to a predefined set of concepts for which previously trained visual concept detectors are available. We adopt the use of Concept Language Models (CLMs), which is a method of augmenting semantic concept definition, and we propose a new concept-selection method for deciding on the appropriate number of the concepts needed to describe an event query. The proposed system achieves state-of-the-art performance in automatic zero-example event detection.&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/H2020/693092/">693092</awardNumber>
      <awardTitle>Training towards a society of data-savvy information professionals to enable open leadership innovation</awardTitle>
    </fundingReference>
    <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>
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