Publikation:

Improving Unsupervised Label Propagation for Pose Tracking and Video Object Segmentation

Lade...
Vorschaubild

Dateien

Waldmann_2-mn1vmh1ak0gf4.pdf
Waldmann_2-mn1vmh1ak0gf4.pdfGröße: 346.38 KBDownloads: 66

Datum

2022

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

ANDRES, Björn, ed. and others. Pattern Recognition : 44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27–30, 2022, Proceedings. Cham: Springer, 2022, pp. 230-245. Lecture Notes in Computer Science. 13485. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-031-16787-4. Available under: doi: 10.1007/978-3-031-16788-1_15

Zusammenfassung

Label propagation is a challenging task in computer vision with many applications. One approach is to learn representations of visual correspondence. In this paper, we study recent works on label propagation based on correspondence, carefully evaluate the effect of various aspects of their implementation, and improve upon various details. Our pipeline assembled from these best practices outperforms the previous state of the art in terms of PCK0.1 on the JHMDB dataset by 6.5%. We also propose a novel joint framework for tracking and keypoint propagation, which in contrast to the core pipeline is applicable to tracking small objects and obtains results that substantially exceed the performance of the core pipeline. Finally, for VOS, we extend the core pipeline to a fully unsupervised one by initializing the first frame with the self-attention layer from DINO. Our pipeline for VOS runs online and can handle static objects. It outperforms unsupervised frameworks with these characteristics.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

4th DAGM German Conference on Pattern Recognition (DAGM GCPR 2022), 27. Sept. 2022 - 30. Sept. 2022, Konstanz
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690WALDMANN, Urs, Jannik BAMBERGER, Ole JOHANNSEN, Oliver DEUSSEN, Bastian GOLDLÜCKE, 2022. Improving Unsupervised Label Propagation for Pose Tracking and Video Object Segmentation. 4th DAGM German Conference on Pattern Recognition (DAGM GCPR 2022). Konstanz, 27. Sept. 2022 - 30. Sept. 2022. In: ANDRES, Björn, ed. and others. Pattern Recognition : 44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27–30, 2022, Proceedings. Cham: Springer, 2022, pp. 230-245. Lecture Notes in Computer Science. 13485. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-031-16787-4. Available under: doi: 10.1007/978-3-031-16788-1_15
BibTex
@inproceedings{Waldmann2022Impro-58701,
  year={2022},
  doi={10.1007/978-3-031-16788-1_15},
  title={Improving Unsupervised Label Propagation for Pose Tracking and Video Object Segmentation},
  number={13485},
  isbn={978-3-031-16787-4},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Pattern Recognition : 44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27–30, 2022, Proceedings},
  pages={230--245},
  editor={Andres, Björn},
  author={Waldmann, Urs and Bamberger, Jannik and Johannsen, Ole and Deussen, Oliver and Goldlücke, Bastian}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/58701">
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/58701"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-09-28T13:26:00Z</dcterms:available>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:language>eng</dc:language>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/58701/1/Waldmann_2-mn1vmh1ak0gf4.pdf"/>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/58701/1/Waldmann_2-mn1vmh1ak0gf4.pdf"/>
    <dc:contributor>Johannsen, Ole</dc:contributor>
    <dc:contributor>Waldmann, Urs</dc:contributor>
    <dc:contributor>Deussen, Oliver</dc:contributor>
    <dc:creator>Goldlücke, Bastian</dc:creator>
    <dcterms:abstract xml:lang="eng">Label propagation is a challenging task in computer vision with many applications. One approach is to learn representations of visual correspondence. In this paper, we study recent works on label propagation based on correspondence, carefully evaluate the effect of various aspects of their implementation, and improve upon various details. Our pipeline assembled from these best practices outperforms the previous state of the art in terms of PCK&lt;sub&gt;0.1&lt;/sub&gt; on the JHMDB dataset by 6.5%. We also propose a novel joint framework for tracking and keypoint propagation, which in contrast to the core pipeline is applicable to tracking small objects and obtains results that substantially exceed the performance of the core pipeline. Finally, for VOS, we extend the core pipeline to a fully unsupervised one by initializing the first frame with the self-attention layer from DINO. Our pipeline for VOS runs online and can handle static objects. It outperforms unsupervised frameworks with these characteristics.</dcterms:abstract>
    <dc:contributor>Bamberger, Jannik</dc:contributor>
    <dcterms:title>Improving Unsupervised Label Propagation for Pose Tracking and Video Object Segmentation</dcterms:title>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Bamberger, Jannik</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-09-28T13:26:00Z</dc:date>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dcterms:issued>2022</dcterms:issued>
    <dc:creator>Johannsen, Ole</dc:creator>
    <dc:creator>Deussen, Oliver</dc:creator>
    <dc:contributor>Goldlücke, Bastian</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Waldmann, Urs</dc:creator>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen