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Memorizing Features Efficiently for Self-supervised Video Object Segmentation

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Bibliographic Details
Published in:Journal of the Brazilian Computer Society
Format: Online Article RSS Article
Published: 2026
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container_title Journal of the Brazilian Computer Society
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:10090
institution FRELIP
journal_source_facet Journal of the Brazilian Computer Society
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Memorizing Features Efficiently for Self-supervised Video Object Segmentation
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title Memorizing Features Efficiently for Self-supervised Video Object Segmentation
title_auth Memorizing Features Efficiently for Self-supervised Video Object Segmentation
title_full Memorizing Features Efficiently for Self-supervised Video Object Segmentation
title_fullStr Memorizing Features Efficiently for Self-supervised Video Object Segmentation
title_full_unstemmed Memorizing Features Efficiently for Self-supervised Video Object Segmentation
title_short Memorizing Features Efficiently for Self-supervised Video Object Segmentation
title_sort memorizing features efficiently for self-supervised video object segmentation
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url https://journals-sol.sbc.org.br/index.php/jbcs/article/view/5904