1 Ocak 2021 Konferans bildirisi Açık Erişim

Gokay, Dilara; Simsar, Enis; Atici, Efehan; Ahmetoglu, Alper; Yuksel, Atif Emre; Yanardag, Pinar

DataCite XML

<?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://aperta.ulakbim.gov.tr/record/237762</identifier>
  <creators>
    <creator>
      <creatorName>Gokay, Dilara</creatorName>
      <givenName>Dilara</givenName>
      <familyName>Gokay</familyName>
      <affiliation>Tech Univ Munich, Munich, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Simsar, Enis</creatorName>
      <givenName>Enis</givenName>
      <familyName>Simsar</familyName>
    </creator>
    <creator>
      <creatorName>Atici, Efehan</creatorName>
      <givenName>Efehan</givenName>
      <familyName>Atici</familyName>
      <affiliation>Bogazici Univ, Bebek, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ahmetoglu, Alper</creatorName>
      <givenName>Alper</givenName>
      <familyName>Ahmetoglu</familyName>
      <affiliation>Bogazici Univ, Bebek, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Yuksel, Atif Emre</creatorName>
      <givenName>Atif Emre</givenName>
      <familyName>Yuksel</familyName>
      <affiliation>Bogazici Univ, Bebek, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Yanardag, Pinar</creatorName>
      <givenName>Pinar</givenName>
      <familyName>Yanardag</familyName>
      <affiliation>Bogazici Univ, Bebek, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Graph2Pix: A Graph-Based Image To Image Translation Framework</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/237762</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/ICCVW54120.2021.00227</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder', where users interpolate multiple GAN-generated images to create artworks. This unique approach of creating new images leads to a tree-like structure where one can track historical data about the creation of a particular image. Inspired by this structure, we propose a novel graph-to-image translation model called Graph2Pix, which takes a graph and corresponding images as input and generates a single image as output. Our experiments show that Graph2Pix is able to outperform several image-to-image translation frameworks on benchmark metrics, including LPIPS (with a 25% improvement) and human perception studies (n = 60), where users preferred the images generated by our method 81.5% of the time.</description>
  </descriptions>
</resource>