Conference paper Open Access

ITI-CERTH participation in TRECVID 2020

Gkountakos, Konstantinos; Galanopoulos, Damianos; Mpakratsas, Marios; Touska, Despoina; Moumtzidou, Anastasia; Ioannidis, Konstantinos; Gialampoukidis, Ilias; Vrochidis, Stefanos; Mezaris, Vasileios; Kompatsiaris, Ioannis


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  <identifier identifierType="DOI">10.5281/zenodo.4506490</identifier>
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    <creator>
      <creatorName>Gkountakos, Konstantinos</creatorName>
      <givenName>Konstantinos</givenName>
      <familyName>Gkountakos</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Galanopoulos, Damianos</creatorName>
      <givenName>Damianos</givenName>
      <familyName>Galanopoulos</familyName>
      <affiliation>CERTH-ITI</affiliation>
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    <creator>
      <creatorName>Mpakratsas, Marios</creatorName>
      <givenName>Marios</givenName>
      <familyName>Mpakratsas</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Touska, Despoina</creatorName>
      <givenName>Despoina</givenName>
      <familyName>Touska</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Moumtzidou, Anastasia</creatorName>
      <givenName>Anastasia</givenName>
      <familyName>Moumtzidou</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Ioannidis, Konstantinos</creatorName>
      <givenName>Konstantinos</givenName>
      <familyName>Ioannidis</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Gialampoukidis, Ilias</creatorName>
      <givenName>Ilias</givenName>
      <familyName>Gialampoukidis</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Vrochidis, Stefanos</creatorName>
      <givenName>Stefanos</givenName>
      <familyName>Vrochidis</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Mezaris, Vasileios</creatorName>
      <givenName>Vasileios</givenName>
      <familyName>Mezaris</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
    <creator>
      <creatorName>Kompatsiaris, Ioannis</creatorName>
      <givenName>Ioannis</givenName>
      <familyName>Kompatsiaris</familyName>
      <affiliation>CERTH-ITI</affiliation>
    </creator>
  </creators>
  <titles>
    <title>ITI-CERTH participation in TRECVID 2020</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-20</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4506490</alternateIdentifier>
  </alternateIdentifiers>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4506489</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/connexions-h2020</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/retv-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://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;This paper provides an overview of the runs submitted to TRECVID 2020 by ITI-CERTH. ITI-CERTH participated in the Ad-hoc Video Search (AVS), Disaster Scene Description and Indexing (DSDI) and Activities in Extended Video (ActEV) tasks. Our AVS task participation is based on an attention-based cross-modal deep network method for retrieving video shots relevant to ad-hoc textual queries. The DSDI task is performed by implementing a multi-label image classification model, trained on all humanly annotated images and estimating the final classes on averaging the predictions on the keyframes of the video shots. For the ActEV task, we deploy an object detection algorithm and then convert the individual detected objects to activities by following an object tracking technique in order to detect human and vehicle-related activities.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
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      <awardTitle>Prediction and Visual Intelligence for Security Information</awardTitle>
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