<?xml version="1.0" encoding="utf-8"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hal="http://hal.archives-ouvertes.fr/" xmlns:gml="http://www.opengis.net/gml/3.3/" xmlns:gmlce="http://www.opengis.net/gml/3.3/ce" version="1.1" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 http://api.archives-ouvertes.fr/documents/aofr-sword.xsd">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title>HAL TEI export of hal-03015764</title>
      </titleStmt>
      <publicationStmt>
        <distributor>CCSD</distributor>
        <availability status="restricted">
          <licence target="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 - Universal</licence>
        </availability>
        <date when="2026-05-26T06:25:13+02:00"/>
      </publicationStmt>
      <sourceDesc>
        <p part="N">HAL API Platform</p>
      </sourceDesc>
    </fileDesc>
  </teiHeader>
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss</title>
            <author role="aut">
              <persName>
                <forename type="first">Yifu</forename>
                <surname>Chen</surname>
              </persName>
              <email type="md5">b2dce2e665067cfa9f719bc9eb2f2de0</email>
              <email type="domain">lip6.fr</email>
              <idno type="idhal" notation="string">yifu-chen</idno>
              <idno type="idhal" notation="numeric">183244</idno>
              <idno type="halauthorid" notation="string">46525-183244</idno>
              <idno type="IDREF">https://www.idref.fr/258759348</idno>
              <affiliation ref="#struct-541720"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Arnaud</forename>
                <surname>Dapogny</surname>
              </persName>
              <idno type="halauthorid">742414-0</idno>
              <affiliation ref="#struct-541720"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Matthieu</forename>
                <surname>Cord</surname>
              </persName>
              <email type="md5">d483a6b89653a05b9e7cf35edbfa0128</email>
              <email type="domain">sorbonne-universite.fr</email>
              <idno type="idhal" notation="string">matthieucord</idno>
              <idno type="idhal" notation="numeric">13617</idno>
              <idno type="halauthorid" notation="string">13283-13617</idno>
              <idno type="IDREF">https://www.idref.fr/132968126</idno>
              <idno type="ORCID">https://orcid.org/0000-0002-0627-5844</idno>
              <affiliation ref="#struct-541720"/>
            </author>
            <editor role="depositor">
              <persName>
                <forename>Sorbonne Université</forename>
                <surname>Gestionnaire HAL 4</surname>
              </persName>
              <email type="md5">b0df5888550d3ab0396b665cb2d17739</email>
              <email type="domain">scd.upmc.fr</email>
            </editor>
            <funder ref="#projanr-42147"/>
          </titleStmt>
          <editionStmt>
            <edition n="v1" type="current">
              <date type="whenSubmitted">2020-11-20 09:20:02</date>
              <date type="whenModified">2024-10-30 13:33:00</date>
              <date type="whenReleased">2020-11-24 14:12:22</date>
              <date type="whenProduced">2020-12</date>
              <date type="whenEndEmbargoed">2021-06-01</date>
              <ref type="file" target="https://hal.sorbonne-universite.fr/hal-03015764v1/document">
                <date notBefore="2021-06-01"/>
              </ref>
              <ref type="file" subtype="author" n="1" target="https://hal.sorbonne-universite.fr/hal-03015764v1/file/Chen%20et%20al.%20-%202020%20-%20SEMEDA%20Enhancing%20segmentation%20precision%20with%20sema.pdf" id="file-3015764-2655407">
                <date notBefore="2021-06-01"/>
              </ref>
              <ref type="externalLink" target="http://arxiv.org/pdf/1905.01892"/>
            </edition>
            <respStmt>
              <resp>contributor</resp>
              <name key="1066187">
                <persName>
                  <forename>Sorbonne Université</forename>
                  <surname>Gestionnaire HAL 4</surname>
                </persName>
                <email type="md5">b0df5888550d3ab0396b665cb2d17739</email>
                <email type="domain">scd.upmc.fr</email>
              </name>
            </respStmt>
          </editionStmt>
          <publicationStmt>
            <distributor>CCSD</distributor>
            <idno type="halId">hal-03015764</idno>
            <idno type="halUri">https://hal.sorbonne-universite.fr/hal-03015764</idno>
            <idno type="halBibtex">chen:hal-03015764</idno>
            <idno type="halRefHtml">&lt;i&gt;Pattern Recognition&lt;/i&gt;, 2020, 108, pp.107557. &lt;a target="_blank" href="https://dx.doi.org/10.1016/j.patcog.2020.107557"&gt;&amp;#x27E8;10.1016/j.patcog.2020.107557&amp;#x27E9;&lt;/a&gt;</idno>
            <idno type="halRef">Pattern Recognition, 2020, 108, pp.107557. &amp;#x27E8;10.1016/j.patcog.2020.107557&amp;#x27E9;</idno>
            <availability status="restricted">
              <licence target="https://about.hal.science/hal-authorisation-v1/">HAL Authorization<ref corresp="#file-3015764-2655407"/></licence>
            </availability>
          </publicationStmt>
          <seriesStmt>
            <idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno>
            <idno type="stamp" n="LIP6" corresp="SORBONNE-UNIVERSITE">Laboratoire d'Informatique de Paris 6</idno>
            <idno type="stamp" n="SORBONNE-UNIVERSITE">Sorbonne Université</idno>
            <idno type="stamp" n="SORBONNE-UNIV" corresp="SORBONNE-UNIVERSITE">Sorbonne Université 01/01/2018</idno>
            <idno type="stamp" n="SU-SCIENCES" corresp="SORBONNE-UNIVERSITE">Faculté des Sciences de Sorbonne Université</idno>
            <idno type="stamp" n="TEST-HALCNRS">Collection test HAL CNRS</idno>
            <idno type="stamp" n="SU-TI">Sorbonne Université - Texte Intégral</idno>
            <idno type="stamp" n="ANR">ANR</idno>
            <idno type="stamp" n="ALLIANCE-SU"> Alliance Sorbonne Université</idno>
            <idno type="stamp" n="SUPRA_MATHS_INFO">Mathématiques + Informatique</idno>
          </seriesStmt>
          <notesStmt>
            <note type="audience" n="2">International</note>
            <note type="popular" n="0">No</note>
            <note type="peer" n="1">Yes</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss</title>
                <author role="aut">
                  <persName>
                    <forename type="first">Yifu</forename>
                    <surname>Chen</surname>
                  </persName>
                  <email type="md5">b2dce2e665067cfa9f719bc9eb2f2de0</email>
                  <email type="domain">lip6.fr</email>
                  <idno type="idhal" notation="string">yifu-chen</idno>
                  <idno type="idhal" notation="numeric">183244</idno>
                  <idno type="halauthorid" notation="string">46525-183244</idno>
                  <idno type="IDREF">https://www.idref.fr/258759348</idno>
                  <affiliation ref="#struct-541720"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Arnaud</forename>
                    <surname>Dapogny</surname>
                  </persName>
                  <idno type="halauthorid">742414-0</idno>
                  <affiliation ref="#struct-541720"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Matthieu</forename>
                    <surname>Cord</surname>
                  </persName>
                  <email type="md5">d483a6b89653a05b9e7cf35edbfa0128</email>
                  <email type="domain">sorbonne-universite.fr</email>
                  <idno type="idhal" notation="string">matthieucord</idno>
                  <idno type="idhal" notation="numeric">13617</idno>
                  <idno type="halauthorid" notation="string">13283-13617</idno>
                  <idno type="IDREF">https://www.idref.fr/132968126</idno>
                  <idno type="ORCID">https://orcid.org/0000-0002-0627-5844</idno>
                  <affiliation ref="#struct-541720"/>
                </author>
              </analytic>
              <monogr>
                <idno type="halJournalId" status="VALID">17798</idno>
                <idno type="issn">0031-3203</idno>
                <title level="j">Pattern Recognition</title>
                <imprint>
                  <publisher>Elsevier</publisher>
                  <biblScope unit="volume">108</biblScope>
                  <biblScope unit="pp">107557</biblScope>
                  <date type="datePub">2020-12</date>
                </imprint>
              </monogr>
              <idno type="doi">10.1016/j.patcog.2020.107557</idno>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <langUsage>
              <language ident="en">English</language>
            </langUsage>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">Semantic Segmentation</term>
                <term xml:lang="en">Loss function</term>
                <term xml:lang="en">Computer vision</term>
              </keywords>
              <classCode scheme="halDomain" n="info">Computer Science [cs]</classCode>
              <classCode scheme="halTypology" n="ART">Journal articles</classCode>
              <classCode scheme="halOldTypology" n="ART">Journal articles</classCode>
              <classCode scheme="halTreeTypology" n="ART">Journal articles</classCode>
            </textClass>
            <abstract xml:lang="en">
              <p>Per-Pixel Cross entropy (PPCE) is a commonly used loss on semantic segmentation tasks. However, it suffers from a number of drawbacks. Firstly, PPCE only depends on the probability of the ground truth class since the latter is usually one-hot encoded. Secondly, PPCE treats all pixels independently and does not take the local structure into account. While perceptual losses (e.g. matching prediction and ground truth in the embedding space of a pre-trained VGG network) would theoretically address these concerns, it does not constitute a practical solution as segmentation masks follow a distribution that differs largely from natural images. In this paper, we introduce a SEMantic EDge-Aware strategy (SEMEDA) to solve these issues. Inspired by perceptual losses, we propose to match the ’probability texture’ of predicted segmentation mask and ground truth through a proxy network trained for semantic edge detection on the ground truth masks. Through thorough experimental validation on several datasets, we show that SEMEDA steadily improves the segmentation accuracy with negligible computational overhead and can be added with any popular segmentation networks in an end-to-end training framework.</p>
            </abstract>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="researchteam" xml:id="struct-541720" status="OLD">
          <orgName>Machine Learning and Information Access</orgName>
          <orgName type="acronym">MLIA</orgName>
          <date type="start">2018-01-01</date>
          <date type="end">2021-12-31</date>
          <desc>
            <address>
              <country key="FR"/>
            </address>
          </desc>
          <listRelation>
            <relation active="#struct-541703" type="direct"/>
            <relation active="#struct-413221" type="indirect"/>
            <relation name="UMR7606" active="#struct-441569" type="indirect"/>
          </listRelation>
        </org>
        <org type="laboratory" xml:id="struct-541703" status="VALID">
          <idno type="IdRef">13558292X</idno>
          <idno type="RNSR">199712651U</idno>
          <idno type="ROR">https://ror.org/05krcen59</idno>
          <orgName>LIP6</orgName>
          <date type="start">2018-01-01</date>
          <desc>
            <address>
              <addrLine>4 Place JUSSIEU 75252 PARIS CEDEX 05</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.lip6.fr/</ref>
          </desc>
          <listRelation>
            <relation active="#struct-413221" type="direct"/>
            <relation name="UMR7606" active="#struct-441569" type="direct"/>
          </listRelation>
        </org>
        <org type="regroupinstitution" xml:id="struct-413221" status="VALID">
          <idno type="IdRef">221333754</idno>
          <idno type="ROR">https://ror.org/02en5vm52</idno>
          <orgName>Sorbonne Université</orgName>
          <orgName type="acronym">SU</orgName>
          <date type="start">2018-01-01</date>
          <desc>
            <address>
              <addrLine>21 rue de l’École de médecine - 75006 Paris</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.sorbonne-universite.fr/</ref>
          </desc>
        </org>
        <org type="regroupinstitution" xml:id="struct-441569" status="VALID">
          <idno type="IdRef">02636817X</idno>
          <idno type="ISNI">0000000122597504</idno>
          <idno type="ROR">https://ror.org/02feahw73</idno>
          <orgName>Centre National de la Recherche Scientifique</orgName>
          <orgName type="acronym">CNRS</orgName>
          <date type="start">1939-10-19</date>
          <desc>
            <address>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.cnrs.fr/</ref>
          </desc>
        </org>
      </listOrg>
      <listOrg type="projects">
        <org type="anrProject" xml:id="projanr-42147" status="VALID">
          <idno type="anr">ANR-16-CE23-0006</idno>
          <orgName>Deep_in_France</orgName>
          <desc>Réseaux de neurones profonds pour l'apprentissage</desc>
          <date type="start">2016</date>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>