Conference paper Open Access

Attention-enhanced Sensorimotor Object Recognition

Thermos, S; Papadopoulos, GT; Daras, P; Potamianos, G


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  <identifier identifierType="URL">https://zenodo.org/record/3727849</identifier>
  <creators>
    <creator>
      <creatorName>Thermos, S</creatorName>
      <givenName>S</givenName>
      <familyName>Thermos</familyName>
    </creator>
    <creator>
      <creatorName>Papadopoulos, GT</creatorName>
      <givenName>GT</givenName>
      <familyName>Papadopoulos</familyName>
    </creator>
    <creator>
      <creatorName>Daras, P</creatorName>
      <givenName>P</givenName>
      <familyName>Daras</familyName>
    </creator>
    <creator>
      <creatorName>Potamianos, G</creatorName>
      <givenName>G</givenName>
      <familyName>Potamianos</familyName>
    </creator>
  </creators>
  <titles>
    <title>Attention-enhanced Sensorimotor Object Recognition</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Sensorimotor object recognition, attention mechanism, stream fusion, deep neural networks</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-10-10</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3727849</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/ICIP.2018.8451158</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/vrtogether-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <version>pre-print</version>
  <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;Sensorimotor learning, namely the process of understanding the physical world by combining visual and motor information, has been recently investigated, achieving promising results for the task of 2D/3D object recognition. Following the recent trend in computer vision, powerful deep neural networks (NNs) have been used to model the &amp;ldquo;sensory&amp;rdquo; and &amp;ldquo;motor&amp;rdquo; information, namely the object appearance and affordance. However, the existing implementations cannot efficiently address the spatio-temporal nature of the humanobject interaction. Inspired by recent work on attention-based learning, this paper introduces an attention-enhanced NN-based model that learns to selectively focus on parts of the physical interaction where the object appearance is corrupted by occlusions and deformations. The model&amp;rsquo;s attention mechanism relies on the confidence of classifying an object based solely on its appearance. Three metrics are used to measure the latter, namely the prediction entropy, the average N-best likelihood difference, and the N-best likelihood dispersion. Evaluation of the attention-enhanced model on the SOR3D dataset reports 33% and 26% relative improvement over the appearance-only and the spatio-temporal fusion baseline models, respectively.&lt;/p&gt;</description>
  </descriptions>
</resource>
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