Conference paper Open Access

A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization

Apostolidis, Evlampios; Metsai, Alexandros; Adamantidou, Eleni; Mezaris, Vasileios; Patras, Ioannis


DCAT Export

<?xml version='1.0' encoding='utf-8'?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#">
  <rdf:Description rdf:about="https://zenodo.org/record/3395967">
    <rdf:type rdf:resource="http://www.w3.org/ns/dcat#Dataset"/>
    <dct:type rdf:resource="http://purl.org/dc/dcmitype/Text"/>
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3395967</dct:identifier>
    <foaf:page rdf:resource="https://zenodo.org/record/3395967"/>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Apostolidis, Evlampios</foaf:name>
        <foaf:givenName>Evlampios</foaf:givenName>
        <foaf:familyName>Apostolidis</foaf:familyName>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>CERTH-ITI, Thermi, Greece, and Queen Mary University of London, UK</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Metsai, Alexandros</foaf:name>
        <foaf:givenName>Alexandros</foaf:givenName>
        <foaf:familyName>Metsai</foaf:familyName>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>CERTH-ITI, Thermi, Greece</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Adamantidou, Eleni</foaf:name>
        <foaf:givenName>Eleni</foaf:givenName>
        <foaf:familyName>Adamantidou</foaf:familyName>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>CERTH-ITI, Thermi, Greece</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Mezaris, Vasileios</foaf:name>
        <foaf:givenName>Vasileios</foaf:givenName>
        <foaf:familyName>Mezaris</foaf:familyName>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>CERTH-ITI, Thermi, Greece</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Patras, Ioannis</foaf:name>
        <foaf:givenName>Ioannis</foaf:givenName>
        <foaf:familyName>Patras</foaf:familyName>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Queen Mary University of London, UK</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:title>A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization</dct:title>
    <dct:publisher>
      <foaf:Agent>
        <foaf:name>Zenodo</foaf:name>
      </foaf:Agent>
    </dct:publisher>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>Video Summarization</dcat:keyword>
    <dcat:keyword>Unsupervised Learning</dcat:keyword>
    <dcat:keyword>Adversarial Training</dcat:keyword>
    <dcat:keyword>Evaluation Protocol</dcat:keyword>
    <dcat:keyword>Datasets</dcat:keyword>
    <frapo:isFundedBy rdf:resource="info:eu-repo/grantAgreement/EC/H2020/780656/"/>
    <schema:funder>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier>
        <foaf:name>European Commission</foaf:name>
      </foaf:Organization>
    </schema:funder>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-10-21</dct:issued>
    <owl:sameAs rdf:resource="https://zenodo.org/record/3395967"/>
    <adms:identifier>
      <adms:Identifier>
        <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3395967</skos:notation>
        <adms:schemeAgency>url</adms:schemeAgency>
      </adms:Identifier>
    </adms:identifier>
    <owl:sameAs rdf:resource="https://doi.org/10.1145/3347449.3357482"/>
    <dct:isPartOf rdf:resource="https://zenodo.org/communities/retv-h2020"/>
    <dct:description>&lt;p&gt;In this paper we present our work on improving the efficiency of adversarial training for unsupervised video summarization. Our starting point is the SUM-GAN model, which creates a representative summary based on the intuition that such a summary should make it possible to reconstruct a video that is indistinguishable from the original one. We build on a publicly available implementation of a variation of this model, that includes a linear compression layer to reduce the number of learned parameters and applies an incremental approach for training the different components of the architecture. After assessing the impact of these changes to the model&amp;rsquo;s performance, we propose a stepwise, label-based learning process to improve the training efficiency of the adversarial part of the model. Before evaluating our model&amp;rsquo;s efficiency, we perform a thorough study with respect to the used evaluation protocols and we examine the possible performance on two benchmarking datasets, namely SumMe and TVSum. Experimental evaluations and comparisons with the state of the art highlight the competitiveness of the proposed method. An ablation study indicates the benefit of each applied change on the model&amp;rsquo;s performance, and points out the advantageous role of the introduced stepwise, label-based training strategy on the learning efficiency of the adversarial part of the architecture.&lt;/p&gt;</dct:description>
    <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/>
    <dct:accessRights>
      <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess">
        <rdfs:label>Open Access</rdfs:label>
      </dct:RightsStatement>
    </dct:accessRights>
    <dcat:distribution>
      <dcat:Distribution>
        <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/>
        <dcat:accessURL rdf:resource="https://zenodo.org/record/3395967"/>
      </dcat:Distribution>
    </dcat:distribution>
    <dcat:distribution>
      <dcat:Distribution>
        <dcat:accessURL>https://doi.org/10.1145/3347449.3357482</dcat:accessURL>
        <dcat:byteSize>1442696</dcat:byteSize>
        <dcat:downloadURL>https://zenodo.org/record/3395967/files/Apostolidis_Summarization.pdf</dcat:downloadURL>
        <dcat:mediaType>application/pdf</dcat:mediaType>
      </dcat:Distribution>
    </dcat:distribution>
  </rdf:Description>
  <foaf:Project rdf:about="info:eu-repo/grantAgreement/EC/H2020/780656/">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">780656</dct:identifier>
    <dct:title>Enhancing and Re-Purposing TV Content for Trans-Vector Engagement</dct:title>
    <frapo:isAwardedBy>
      <foaf:Organization>
        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier>
        <foaf:name>European Commission</foaf:name>
      </foaf:Organization>
    </frapo:isAwardedBy>
  </foaf:Project>
</rdf:RDF>
303
63
views
downloads
Views 303
Downloads 63
Data volume 90.9 MB
Unique views 291
Unique downloads 57

Share

Cite as