Conference paper Open Access

Incorporating Textual Similarity in Video Captioning Schemes

Gkountakos, Konstantinos; Dimou, Anastasios; Papadopoulos, Georgios Th.; Daras, Petros


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/3560519</identifier>
  <creators>
    <creator>
      <creatorName>Gkountakos, Konstantinos</creatorName>
      <givenName>Konstantinos</givenName>
      <familyName>Gkountakos</familyName>
      <affiliation>CERTH</affiliation>
    </creator>
    <creator>
      <creatorName>Dimou, Anastasios</creatorName>
      <givenName>Anastasios</givenName>
      <familyName>Dimou</familyName>
      <affiliation>CERTH</affiliation>
    </creator>
    <creator>
      <creatorName>Papadopoulos, Georgios Th.</creatorName>
      <givenName>Georgios Th.</givenName>
      <familyName>Papadopoulos</familyName>
      <affiliation>CERTH</affiliation>
    </creator>
    <creator>
      <creatorName>Daras, Petros</creatorName>
      <givenName>Petros</givenName>
      <familyName>Daras</familyName>
    </creator>
  </creators>
  <titles>
    <title>Incorporating Textual Similarity in Video Captioning Schemes</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Video captioning</subject>
    <subject>Word2Vec</subject>
    <subject>Textual information</subject>
    <subject>Encoder-decoder</subject>
    <subject>Recurrent Neural Network</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-08-12</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3560519</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/ICE.2019.8792602</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;The problem of video captioning has been heavily investigated from the research community the last years and, especially, since Recurrent Neural Networks (RNNs) have been introduced. Aforementioned approaches of video captioning, are usually based on sequence-to-sequence models that aim to exploit the visual information by detecting events, objects, or via matching entities to words. However, the exploitation of the contextual information that can be extracted from the vocabulary has not been investigated yet, except from approaches that make use of parts of speech such as verbs, nouns, and adjectives. The proposed approach is based on the assumption that textually similar captions should represent similar visual content. Specifically, we propose a novel loss function that penalizes/rewards the wrong/correct predicted words based on the semantic cluster that they belong to. The proposed method is evaluated using two widely-known datasets in the video captioning domain, Microsoft Research - Video to Text (MSR-VTT) and Microsoft Research Video Description Corpus (MSVD). Finally, experimental analysis proves that the proposed method outperforms the baseline approach in most cases.&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/H2020/787061/">787061</awardNumber>
      <awardTitle>Advanced tools for fighting oNline Illegal TrAfficking</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
25
18
views
downloads
Views 25
Downloads 18
Data volume 7.1 MB
Unique views 20
Unique downloads 18

Share

Cite as