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[PDF] Domain-Agnostic Prior for Transfer Semantic Segmentation | Semantic Scholar
https://www.semanticscholar.org/paper/edf9f8a9311236e72ba480f5ab206f6aa3256b8d
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</div></header><div class="network-status network-status--is-online"/></div></div><div class="app-page__content"><div class="fresh-paper-detail-page paper-detail-cards" role="main" id="main-content" data-heap-id="new_ab_framework_mock_ab" data-heap-group="control"><div class="fresh-paper-detail-page__above-the-fold"><div class="centered-max-width-content"><div class="flex-container flex-row flex-column-med flex-relative-pos fresh-paper-detail-page__top-container"><div class="flex-item flex-item--width-66 flex-item__left-column"><div class="fresh-paper-detail-page__header__mobile" data-test-id="paper-detail-page-header"><ul class="paper-meta paper-detail__paper-meta-top" data-test-id="paper-meta-subhead"><li data-test-id="paper-doi"><section class="doi"><span class="doi__label">DOI:</span><a class="doi__link" href="https://doi.org/10.1109/CVPR52688.2022.00694">10.1109/CVPR52688.2022.00694</a></section></li><li data-test-id="corpus-id">Corpus ID: 247996730</li></ul><h1 data-test-id="paper-detail-title">Domain-Agnostic Prior for Transfer Semantic Segmentation</h1><pre class="bibtex-citation" data-nosnippet="true">@article{Huo2022DomainAgnosticPF, title={Domain-Agnostic Prior for Transfer Semantic Segmentation}, author={Xinyue Huo and Lingxi Xie and Hengtong Hu and Wen-gang Zhou and Houqiang Li and Qi Tian}, journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2022}, pages={7065-7075}, url={https://api.semanticscholar.org/CorpusID:247996730} }</pre><ul class="flex-row-vcenter paper-meta"><li class="paper-meta-item"><span class="paper-meta" data-test-id="paper-meta-subhead"><span class="author-list"><span data-heap-id="heap_author_list_item" data-heap-author-id="91523299" data-test-id="author-list"><a class="author-list__link author-list__author-name" href="/author/Xinyue-Huo/91523299"><span class=""><span>Xinyue Huo</span></span></a></span><span data-heap-id="heap_author_list_item" data-heap-author-id="3041937" data-test-id="author-list"><span aria-hidden="true">, </span><a class="author-list__link author-list__author-name" href="/author/Lingxi-Xie/3041937"><span class=""><span>Lingxi Xie</span></span></a></span><span data-heap-id="heap_author_list_item" data-heap-author-id="1400120070" data-test-id="author-list"><span aria-hidden="true">, </span><span><button data-test-id="author-list-expand" aria-expanded="false" aria-label="Show 3 more authors" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default more-authors-label"><span class="cl-button__label">+3 authors</span></button> </span><a class="author-list__link author-list__author-name" href="/author/Qi-Tian/1400120070"><span class=""><span>Qi Tian</span></span></a></span></span></span></li><li class="paper-meta-item"><span data-test-id="year-and-venue">Published<!-- --> in <span data-test-id="venue-metadata"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-test-id="normalized-venue-link" data-heap-id="venue_serp_link_click" href="/venue?name=Computer%20Vision%20and%20Pattern%20Recognition"><span class="" data-test-id="venue-metadata"><span>Computer Vision and Pattern…</span></span></a></span> <span data-test-id="paper-year"><span class=""><span>6 April 2022</span></span></span></span></li><li class="paper-meta-item">Computer Science</li></ul><div class="tldr-abstract-replacement text-truncator paper-detail-page__tldr-abstract"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>This paper presents a simple and effective mechanism that regularizes cross-domain representation learning with a domain-agnostic prior (DAP) that constrains the features extracted from source and target domains to align with a Domainagnostic space.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="flex-container flex-wrap flex-paper-actions__group"><div class="flex-item alternate-sources"><div class="flex-container 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class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/0"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/1-Figure1-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/1-Figure1-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/1-Figure1-1.png" alt="figure 1" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">figure 1</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/1"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/4-Table1-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/4-Table1-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/4-Table1-1.png" alt="table 1" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">table 1</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/2"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/3-Figure2-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/3-Figure2-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/3-Figure2-1.png" alt="figure 2" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">figure 2</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/3"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/6-Table2-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/6-Table2-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/6-Table2-1.png" alt="table 2" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">table 2</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/4"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/5-Figure3-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/5-Figure3-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/5-Figure3-1.png" alt="figure 3" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">figure 3</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/5"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/6-Table3-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/6-Table3-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/6-Table3-1.png" alt="table 3" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">table 3</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/6"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/7-Figure4-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/7-Figure4-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/7-Figure4-1.png" alt="figure 4" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">figure 4</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/7"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/7-Table4-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/7-Table4-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/7-Table4-1.png" alt="table 4" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">table 4</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/8"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/7-Figure5-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/7-Figure5-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/7-Figure5-1.png" alt="figure 5" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">figure 5</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/9"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/8-Table5-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/8-Table5-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/8-Table5-1.png" alt="table 5" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">table 5</figcaption></figure></a></li><li class="figure-list__figure" data-test-id="figure-list-item"><a class="figure-list__figure_link" tabindex="0" href="/paper/Domain-Agnostic-Prior-for-Transfer-Semantic-Huo-Xie/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/figure/10"><figure><div class="figure-list__figure-thumb"><img class="figure-list__figure-image" srcset="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/250px/8-Table6-1.png 1x,https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/500px/8-Table6-1.png 2x" src="https://d3i71xaburhd42.cloudfront.net/edf9f8a9311236e72ba480f5ab206f6aa3256b8d/8-Table6-1.png" alt="table 6" data-test-id="figure-thumbnail-img" loading="lazy"/></div><figcaption class="figure-list__figure-caption">table 6</figcaption></figure></a></li></ul><div class="figure-list__pagination"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default"><span class="cl-button__label">View All 11 Figures & Tables</span></button></div></div></div></div></div><div><div id="paper-topics" class="card card__mobile card--type-experimental" tabindex="0"><div class="card-header card-header__mobile card-header--hidden-border"><div class="card-header-title"><h2 class="card-header-title__title">Topics</h2></div></div><div class="card-content card-content__mobile card-content__children-own-layout"><div class="card-content-main paper-topics__container"><a 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class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="ebf77c7881d06702376c1f7cf4a7ed38262e61e8" data-heap-citation-type="citingPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Improve-Cross-domain-Mixed-Sampling-with-Guidance-Zhou-Zhou/ebf77c7881d06702376c1f7cf4a7ed38262e61e8"><h3 id="paper-ebf77c7881d06702376c1f7cf4a7ed38262e61e8" class="cl-paper-title" tabindex="" aria-describedby="">Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="152326064" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Wenlve-Zhou/152326064"><span class=""><span>Wenlve Zhou</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2149135897" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Zhiheng-Zhou/2149135897"><span class=""><span>Zhiheng Zhou</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2240503173" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Tianlei-Wang/2240503173"><span class=""><span>Tianlei Wang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2249210323" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Delu-Zeng/2249210323"><span class=""><span>Delu Zeng</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2024</span></span></li></ul><div class="cl-paper-abstract tldr__paper-card"><span class="" data-test-id="paper-abstract-toggle"><span>Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source domain to perform well on a target domain without requiring additional annotations. 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cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">2 Excerpts</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="09e94d71ab4ddd002ed62b376062c835621a5b11" data-heap-citation-type="citingPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/MIC%3A-Masked-Image-Consistency-for-Context-Enhanced-Hoyer-Dai/09e94d71ab4ddd002ed62b376062c835621a5b11"><h3 id="paper-09e94d71ab4ddd002ed62b376062c835621a5b11" class="cl-paper-title" tabindex="" aria-describedby="">MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="83246531" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Lukas-Hoyer/83246531"><span class=""><span>Lukas Hoyer</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1778526" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Dengxin-Dai/1778526"><span class=""><span>Dengxin Dai</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2109226340" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Haoran-Wang/2109226340"><span class=""><span>Haoran Wang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1681236" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/L.-Gool/1681236"><span class=""><span>L. Gool</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=Computer%20Vision%20and%20Pattern%20Recognition">Computer Vision and Pattern Recognition</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>A Masked Image Consistency module to enhance UDA by learning spatial context relations of the target domain as additional clues for robust visual recognition and significantly improves the state-of-the-art performance across the different recognition tasks for synthetic- to-real, day-to-nighttime, and clear-toadverse-weather UDA.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row 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class="cl-paper-title" tabindex="" aria-describedby="">Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2135332116" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Elham-Amin-Mansour/2135332116"><span class=""><span>Elham Amin Mansour</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="147758444" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Ozan-Unal/147758444"><span class=""><span>Ozan Unal</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2087117151" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Suman-Saha/2087117151"><span class=""><span>Suman Saha</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2295515403" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Benjamin-Bejar/2295515403"><span class=""><span>Benjamin Bejar</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2246990749" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/L.-V.-Gool/2246990749"><span class=""><span>L. V. Gool</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=arXiv.org">arXiv.org</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2024</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>This work focuses on incorporating instance-level adaptation via a novel instance-aware cross-domain mixing strategy IMix, and presents an end-to-end model incorporating these two mechanisms called LIDAPS, achieving state-of-the-art results on all popular panoptic UDA benchmarks.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div></div></div><div class="cl-container__footer"><div class="cl-paper-card__footer"><div class="flex-row cl-paper-actions"><div class="cl-paper-action__button-container"><button 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data-heap-has-intents="false" data-test-id="citation-paper-title" href="/paper/Bidirectional-Domain-Mixup-for-Domain-Adaptive-Kim-Seo/a3577255e136a2c38198ac240cec2921bd739cde"><h3 id="paper-a3577255e136a2c38198ac240cec2921bd739cde" class="cl-paper-title" tabindex="" aria-describedby="">Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2111926793" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Daehan-Kim/2111926793"><span class=""><span>Daehan Kim</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2059726195" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Min-seok-Seo/2059726195"><span class=""><span>Min-seok Seo</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2854596" data-test-id="author-list"><span><button data-test-id="author-list-expand" aria-expanded="false" aria-label="Show 4 more authors" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default cl-paper-authors__toggle cl-paper-authors__toggle__with-background"><span class="cl-button__label">+4 authors</span></button> </span><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Dong-geol-Choi/2854596"><span class=""><span>Dong-geol Choi</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=AAAI%20Conference%20on%20Artificial%20Intelligence">AAAI Conference on Artificial Intelligence</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>BDM leaves domain transferable regions by cutting, balances the dataset-level class distribution while preserving natural scene context by pasting, and jointly considers class distribution, spatial structure, and pseudo label confidence.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row cl-paper-controls"><div class="cl-paper-controls__stats"><ul class="cl-paper-stats-list" data-test-id="citation-stats"><li aria-label="5 Citations" class="cl-paper-stats__item"><a aria-label="Visit citations for this paper" href="/paper/a3577255e136a2c38198ac240cec2921bd739cde#citing-papers" 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class="cl-paper-card__header"><div class="cl-paper-flags" data-test-id="citation"><div class="citation__body"><div class="cl-paper-flags__paper-card"><div class="cl-paper-flags__paper-card__flag"><button data-heap-id="citation_box" data-heap-paper-id="f74104d26c22b0474efe29fcdabaad488ba55329" data-heap-type="excerpt" data-heap-citation-type="citingPapers" data-test-id="excerpt" aria-expanded="false" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">2 Excerpts</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="f74104d26c22b0474efe29fcdabaad488ba55329" data-heap-citation-type="citingPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Focus-on-Your-Target%3A-A-Dual-Teacher-Student-for-Huo-Xie/f74104d26c22b0474efe29fcdabaad488ba55329"><h3 id="paper-f74104d26c22b0474efe29fcdabaad488ba55329" class="cl-paper-title" tabindex="" aria-describedby="">Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="91523299" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Xinyue-Huo/91523299"><span class=""><span>Xinyue Huo</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="3041937" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Lingxi-Xie/3041937"><span class=""><span>Lingxi Xie</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="38272296" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Wen-gang-Zhou/38272296"><span class=""><span>Wen-gang Zhou</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2144406784" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Houqiang-Li/2144406784"><span class=""><span>Houqiang Li</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1400120070" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Qi-Tian/1400120070"><span class=""><span>Qi Tian</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=IEEE%20International%20Conference%20on%20Computer%20Vision">IEEE International Conference on Computer Vision</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>A novel dual teacher-student (DTS) framework is proposed and equip it with a bidirectional learning strategy, where the second teacher-student model learns to ‘Focus on Your Target’ while the first model is not affected.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div 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cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">1 Excerpt</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="ea75982c61485e7cfab53359a8195f83b7ab31aa" data-heap-citation-type="citingPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Diffusion-based-Image-Translation-with-Label-for-Peng-Hu/ea75982c61485e7cfab53359a8195f83b7ab31aa"><h3 id="paper-ea75982c61485e7cfab53359a8195f83b7ab31aa" class="cl-paper-title" tabindex="" aria-describedby="">Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2067913944" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Duo-Peng/2067913944"><span class=""><span>Duo Peng</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2067770617" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Ping-Hu/2067770617"><span class=""><span>Ping Hu</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2066160726" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Qiuhong-Ke/2066160726"><span class=""><span>Qiuhong Ke</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="40478976" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/J.-Liu/40478976"><span class=""><span>J. Liu</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=IEEE%20International%20Conference%20on%20Computer%20Vision">IEEE International Conference on Computer Vision</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>This work forms cross-domain image translation as a denoising diffusion process and utilizes a novel Semantic Gradient Guidance (SGG) method to constrain the translation process, conditioning it on the pixel-wise source labels.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row cl-paper-controls"><div class="cl-paper-controls__stats"><ul class="cl-paper-stats-list" data-test-id="citation-stats"><li aria-label="8 Citations" class="cl-paper-stats__item"><a aria-label="Visit citations for this paper" href="/paper/ea75982c61485e7cfab53359a8195f83b7ab31aa#citing-papers" class="cl-paper-stats__citation-pdp-link"><div class="cl-paper-stat cl-paper-stats__mobile-badge cl-paper-stat__with-border"><svg aria-hidden="true" width="12" height="12" class="cl-paper-stats__icon icon-svg icon-cited-by" alt="" data-test-id="icon-cited-by"><use xlink:href="#cited-by"/></svg>8<!-- --> Citations</div></a></li><li class="cl-paper-stats__item"><div class="flex-row paper-badge-list"><a class="link-button--no-underline" href="/paper/ea75982c61485e7cfab53359a8195f83b7ab31aa"><div class="cl-paper-stat cl-paper-stat__collapsable cl-paper-stat__icon-only cl-paper-stat__adjust-badge-position cl-paper-stats__badge__open-access cl-paper-stats__badge-hover cl-paper-stats__mobile-badge" data-test-id="paper-badge-open_access" aria-label="This paper has 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data-paper-id="cf69b9a02bb67a737ea9237dbbc7c33bb431310b" class="cl-container cl-container--has-footer cl-card-container cl-paper-card citation-list__paper-card"><div class="cl-container__content"><div class="cl-paper-card__header"><div class="cl-paper-flags" data-test-id="citation"><div class="citation__body"><div class="cl-paper-flags__paper-card"><div class="cl-paper-flags__paper-card__flag"><button data-heap-id="citation_box" data-heap-paper-id="cf69b9a02bb67a737ea9237dbbc7c33bb431310b" data-heap-type="excerpt" data-heap-citation-type="citingPapers" data-test-id="excerpt" aria-expanded="false" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">3 Excerpts</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="cf69b9a02bb67a737ea9237dbbc7c33bb431310b" data-heap-citation-type="citingPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Domain-Adaptive-Video-Semantic-Segmentation-via-Cho-Lee/cf69b9a02bb67a737ea9237dbbc7c33bb431310b"><h3 id="paper-cf69b9a02bb67a737ea9237dbbc7c33bb431310b" class="cl-paper-title" tabindex="" aria-describedby="">Domain Adaptive Video Semantic Segmentation via Cross-Domain Moving Object Mixing</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2190048379" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Kyusik-Cho/2190048379"><span class=""><span>Kyusik Cho</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2108124330" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Suhyeon-Lee/2108124330"><span class=""><span>Suhyeon Lee</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="79755154" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Hongje-Seong/79755154"><span class=""><span>Hongje Seong</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1842453" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Euntai-Kim/1842453"><span class=""><span>Euntai Kim</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=IEEE%20Workshop%2FWinter%20Conference%20on%20Applications%20of%20Computer%20Vision">IEEE Workshop/Winter Conference on Applications…</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>Cross-domain Moving Object Mixing (CMOM) is proposed that cuts several objects, including hard-to-transfer classes, in the source domain video clip and pastes them into the targetdomain video clip, and Feature Alignment with Temporal Context (FATC) is presented to enhance target domain feature discriminability.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row cl-paper-controls"><div class="cl-paper-controls__stats"><ul class="cl-paper-stats-list" data-test-id="citation-stats"><li aria-label="2 Citations" class="cl-paper-stats__item"><a aria-label="Visit citations for this paper" href="/paper/cf69b9a02bb67a737ea9237dbbc7c33bb431310b#citing-papers" class="cl-paper-stats__citation-pdp-link"><div class="cl-paper-stat cl-paper-stats__mobile-badge cl-paper-stat__with-border"><svg aria-hidden="true" width="12" height="12" class="cl-paper-stats__icon icon-svg icon-cited-by" alt="" data-test-id="icon-cited-by"><use xlink:href="#cited-by"/></svg>2<!-- --> Citations</div></a></li><li class="cl-paper-stats__item"><div class="flex-row paper-badge-list"><a class="link-button--no-underline" href="/paper/cf69b9a02bb67a737ea9237dbbc7c33bb431310b"><div class="cl-paper-stat cl-paper-stat__collapsable cl-paper-stat__icon-only cl-paper-stat__adjust-badge-position cl-paper-stats__badge__unpaywall cl-paper-stats__badge-hover 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cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">2 Excerpts</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="63ebf7e34e73bd6528eeef5a0c48876e6d298bf9" data-heap-citation-type="citingPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Learning-Pseudo-Relations-for-Cross-domain-Semantic-Zhao-Wang/63ebf7e34e73bd6528eeef5a0c48876e6d298bf9"><h3 id="paper-63ebf7e34e73bd6528eeef5a0c48876e6d298bf9" class="cl-paper-title" tabindex="" aria-describedby="">Learning Pseudo-Relations for Cross-domain Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2167260407" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Dong-Zhao/2167260407"><span class=""><span>Dong Zhao</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2273993814" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Shuang-Wang/2273993814"><span class=""><span>Shuang Wang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2260698043" data-test-id="author-list"><span><button data-test-id="author-list-expand" aria-expanded="false" aria-label="Show 4 more authors" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default cl-paper-authors__toggle cl-paper-authors__toggle__with-background"><span class="cl-button__label">+4 authors</span></button> </span><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Licheng-Jiao/2260698043"><span class=""><span>Licheng Jiao</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=IEEE%20International%20Conference%20on%20Computer%20Vision">IEEE International Conference on Computer Vision</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>A pseudo-relation learning framework, Relation Teacher (RTea), which can exploitable pixel relations to efficiently use unreliable pixels and learn generalized representations and the cross-domain inadaptation is eliminated.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row cl-paper-controls"><div class="cl-paper-controls__stats"><ul class="cl-paper-stats-list" data-test-id="citation-stats"><li aria-label="3 Citations" class="cl-paper-stats__item"><a aria-label="Visit citations for this paper" href="/paper/63ebf7e34e73bd6528eeef5a0c48876e6d298bf9#citing-papers" class="cl-paper-stats__citation-pdp-link"><div class="cl-paper-stat cl-paper-stats__mobile-badge"><svg aria-hidden="true" width="12" height="12" class="cl-paper-stats__icon icon-svg icon-cited-by" alt="" data-test-id="icon-cited-by"><use 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href="/paper/Survey-on-Unsupervised-Domain-Adaptation-for-for-in-Schwonberg-Niemeijer/ad0f06ea54b93bb3e18c85ca6826bde0896786da"><h3 id="paper-ad0f06ea54b93bb3e18c85ca6826bde0896786da" class="cl-paper-title" tabindex="" aria-describedby="">Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2105055642" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Manuel-Schwonberg/2105055642"><span class=""><span>Manuel Schwonberg</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="71209827" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/J.-Niemeijer/71209827"><span class=""><span>J. Niemeijer</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1679795" data-test-id="author-list"><span><button data-test-id="author-list-expand" aria-expanded="false" aria-label="Show 4 more authors" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default cl-paper-authors__toggle cl-paper-authors__toggle__with-background"><span class="cl-button__label">+4 authors</span></button> </span><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/T.-Fingscheidt/1679795"><span class=""><span>T. Fingscheidt</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science, Engineering</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=IEEE%20Access">IEEE Access</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>This survey aims to facilitate UDA research further and encourage scientists to exploit novel research directions, as it presents an overview of the current state of the art and conducts a critical analysis of the state-of-the-art.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row cl-paper-controls"><div class="cl-paper-controls__stats"><ul class="cl-paper-stats-list" data-test-id="citation-stats"><li aria-label="15 Citations" 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cl-card-container cl-paper-card citation-list__paper-card"><div class="cl-container__content"><div class="cl-paper-card__header"><div class="cl-paper-flags" data-test-id="citation"><div class="citation__body"><div class="cl-paper-flags__paper-card"><div class="cl-paper-flags__paper-card__flag"><button data-heap-id="citation_box" data-heap-paper-id="8f7e29caf3f14271009060d2afde8da8b955df9f" data-heap-type="excerpt" data-heap-citation-type="citingPapers" data-test-id="excerpt" aria-expanded="false" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">5 Excerpts</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="8f7e29caf3f14271009060d2afde8da8b955df9f" data-heap-citation-type="citingPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Enhancing-Discriminative-Ability-among-Similar-with-Lee-Oh/8f7e29caf3f14271009060d2afde8da8b955df9f"><h3 id="paper-8f7e29caf3f14271009060d2afde8da8b955df9f" class="cl-paper-title" tabindex="" aria-describedby="">Enhancing Discriminative Ability among Similar Classes with Guidance of Text-Image Correlation for Unsupervised Domain Adaptation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2226491040" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Yu%E2%80%93Won-Lee/2226491040"><span class=""><span>Yu–Won Lee</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2106414581" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Myeong-Seok-Oh/2106414581"><span class=""><span>Myeong-Seok Oh</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2192361790" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Ho-Joong-Kim/2192361790"><span class=""><span>Ho-Joong Kim</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="72490748" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Seonghyeon-Lee/72490748"><span class=""><span>Seonghyeon Lee</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=IEEE%20International%20Joint%20Conference%20on%20Neural%20Network">IEEE International Joint Conference on Neural…</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>A versatile approach based on text-image correlation-guided domain adaptation (TigDA), which maintains a distance to properly adjust the decision boundaries between classes in the feature space, which achieved the highest performance among other UDA methods in both single-resolution and multi-resolution cases.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div></div></div><div class="cl-container__footer"><div 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class="citation-list__citations"><div data-paper-id="532ca0ed6f8f1f2814b270d56a6ebe21501af08b" class="cl-container cl-container--has-footer cl-card-container cl-paper-card citation-list__paper-card"><div class="cl-container__content"><div class="cl-paper-card__header"><div class="cl-paper-flags" data-test-id="citation"><div class="citation__body"><div class="cl-paper-flags__paper-card"><div class="cl-paper-flags__paper-card__flag"><button data-heap-id="citation_box" data-heap-paper-id="532ca0ed6f8f1f2814b270d56a6ebe21501af08b" data-heap-type="excerpt" data-heap-citation-type="citedPapers" data-test-id="excerpt" aria-expanded="false" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg 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class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Chaoqi-Chen/2145762695"><span class=""><span>Chaoqi Chen</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2113722346" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Weiping-Xie/2113722346"><span class=""><span>Weiping Xie</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1768190" data-test-id="author-list"><span><button data-test-id="author-list-expand" aria-expanded="false" aria-label="Show 5 more authors" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default cl-paper-authors__toggle cl-paper-authors__toggle__with-background"><span class="cl-button__label">+5 authors</span></button> </span><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Junzhou-Huang/1768190"><span class=""><span>Junzhou Huang</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=Computer%20Vision%20and%20Pattern%20Recognition">Computer Vision and Pattern Recognition</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2019</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>The Progressive Feature Alignment Network (PFAN) is proposed to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable 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href="/paper/MetaCorrection%3A-Domain-aware-Meta-Loss-Correction-Guo-Yang/2713adc5d2d043054f4346c647103017e012eade"><h3 id="paper-2713adc5d2d043054f4346c647103017e012eade" class="cl-paper-title" tabindex="" aria-describedby="">MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="48421737" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Xiaoqing-Guo/48421737"><span class=""><span>Xiaoqing Guo</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2007498924" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Chen-Yang/2007498924"><span class=""><span>Chen Yang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2118423812" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Baopu-Li/2118423812"><span class=""><span>Baopu Li</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="3080513" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Yixuan-Yuan/3080513"><span class=""><span>Yixuan Yuan</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=Computer%20Vision%20and%20Pattern%20Recognition">Computer Vision and Pattern Recognition</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2021</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>A MetaCorrection framework is advanced, where a Domain-aware Meta-learning strategy is devised to benefit Loss Correction (DMLC) for UDA semantic segmentation and the proposed DMLC strategy is employed to provide matched and compatible supervision signals for different level features, thereby ensuring deep adaptation.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row cl-paper-controls"><div class="cl-paper-controls__stats"><ul class="cl-paper-stats-list" data-test-id="citation-stats"><li aria-label="64 Citations" class="cl-paper-stats__item"><a aria-label="Visit citations for this paper" href="/paper/2713adc5d2d043054f4346c647103017e012eade#citing-papers" class="cl-paper-stats__citation-pdp-link"><div class="cl-paper-stat cl-paper-stats__mobile-badge cl-paper-stat__with-border"><svg aria-hidden="true" width="12" height="12" class="cl-paper-stats__icon 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Toldo</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="30175138" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Umberto-Michieli/30175138"><span class=""><span>Umberto Michieli</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="27507167" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Gianluca-Agresti/27507167"><span class=""><span>Gianluca Agresti</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1709035" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/P.-Zanuttigh/1709035"><span class=""><span>P. Zanuttigh</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=Image%20and%20Vision%20Computing">Image and Vision Computing</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2020</span></span></li></ul><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row cl-paper-controls"><div class="cl-paper-controls__stats"><ul class="cl-paper-stats-list" data-test-id="citation-stats"><li aria-label="48 Citations" class="cl-paper-stats__item"><a aria-label="Visit citations for this paper" href="/paper/3b5d9f6b586372b85be89dda3c5b9ccdeda40ad3#citing-papers" class="cl-paper-stats__citation-pdp-link"><div class="cl-paper-stat cl-paper-stats__mobile-badge cl-paper-stat__with-border"><svg 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data-heap-paper-id="9b15362b9a025071aa170f7ed81a761bc057c859" data-heap-citation-type="citedPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Unsupervised-Domain-Adaptation-for-Semantic-via-Zou-Yu/9b15362b9a025071aa170f7ed81a761bc057c859"><h3 id="paper-9b15362b9a025071aa170f7ed81a761bc057c859" class="cl-paper-title" tabindex="" aria-describedby="">Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2087067777" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Yang-Zou/2087067777"><span class=""><span>Yang Zou</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1751019" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Zhiding-Yu/1751019"><span class=""><span>Zhiding Yu</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="133728653" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/B.-V.-Vijaya-Kumar/133728653"><span class=""><span>B. V. Vijaya Kumar</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2110201006" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Jinsong-Wang/2110201006"><span class=""><span>Jinsong Wang</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=European%20Conference%20on%20Computer%20Vision">European Conference on Computer Vision</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2018</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>This paper proposes a novel UDA framework based on an iterative self-training (ST) procedure, where the problem is formulated as latent variable loss minimization, and can be solved by alternatively generating pseudo labels on target data and re-training the model with these labels.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left 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cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">1 Excerpt</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="756da25dcebfdc4914f110bbe99e14e7c07cb0c2" data-heap-citation-type="citedPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/DSP%3A-Dual-Soft-Paste-for-Unsupervised-Domain-Gao-Zhang/756da25dcebfdc4914f110bbe99e14e7c07cb0c2"><h3 id="paper-756da25dcebfdc4914f110bbe99e14e7c07cb0c2" class="cl-paper-title" tabindex="" aria-describedby="">DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2148991565" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Li-Gao/2148991565"><span class=""><span>Li Gao</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1519066969" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Jing-Zhang/1519066969"><span class=""><span>Jing Zhang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2107901992" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Lefei-Zhang/2107901992"><span class=""><span>Lefei Zhang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="143719920" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/D.-Tao/143719920"><span class=""><span>D. Tao</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=ACM%20Multimedia">ACM Multimedia</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2021</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>A novel Dual Soft-Paste method that facilitates the model learning domain-invariant features from the intermediate domains, leading to faster convergence and better performance.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div class="cl-paper__bulleted-row cl-paper-controls"><div class="cl-paper-controls__stats"><ul class="cl-paper-stats-list" data-test-id="citation-stats"><li aria-label="62 Citations" class="cl-paper-stats__item"><a aria-label="Visit citations for 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class="cl-container__content"><div class="cl-paper-card__header"><div class="cl-paper-flags" data-test-id="citation"><div class="citation__body"><div class="cl-paper-flags__paper-card"><div class="cl-paper-flags__paper-card__flag"><button data-heap-id="citation_box" data-heap-paper-id="48764f8f64986cf690ce0f1040b2e6bbf06fd23c" data-heap-type="excerpt" data-heap-citation-type="citedPapers" data-test-id="excerpt" aria-expanded="false" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">6 Excerpts</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="48764f8f64986cf690ce0f1040b2e6bbf06fd23c" data-heap-citation-type="citedPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Prototypical-Pseudo-Label-Denoising-and-Target-for-Zhang-Zhang/48764f8f64986cf690ce0f1040b2e6bbf06fd23c"><h3 id="paper-48764f8f64986cf690ce0f1040b2e6bbf06fd23c" class="cl-paper-title" tabindex="" aria-describedby="">Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2143362134" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Pan-Zhang/2143362134"><span class=""><span>Pan Zhang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="145803569" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Bo-Zhang/145803569"><span class=""><span>Bo Zhang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2146320438" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Ting-Zhang/2146320438"><span class=""><span>Ting Zhang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="47514557" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Dong-Chen/47514557"><span class=""><span>Dong Chen</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2153952083" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Yong-Wang/2153952083"><span class=""><span>Yong Wang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1716835" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Fang-Wen/1716835"><span class=""><span>Fang Wen</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=Computer%20Vision%20and%20Pattern%20Recognition">Computer Vision and Pattern Recognition</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2021</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>This paper relies on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation and exploits the feature distances from prototypes that provide richer information than mere prototypes to exploit the likelihood of pseudo labels.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider 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data-heap-paper-id="f15aecec2a5672beaae77853ea0eea560505df8e" data-heap-citation-type="citedPapers" data-heap-has-intents="false" data-test-id="citation-paper-title" href="/paper/FDA%3A-Fourier-Domain-Adaptation-for-Semantic-Yang-Soatto/f15aecec2a5672beaae77853ea0eea560505df8e"><h3 id="paper-f15aecec2a5672beaae77853ea0eea560505df8e" class="cl-paper-title" tabindex="" aria-describedby="">FDA: Fourier Domain Adaptation for Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2881401" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Yanchao-Yang/2881401"><span class=""><span>Yanchao Yang</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1715959" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Stefano-Soatto/1715959"><span class=""><span>Stefano Soatto</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=Computer%20Vision%20and%20Pattern%20Recognition">Computer Vision and Pattern Recognition</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2020</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>A simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other, which results indicate that even simple procedures can discount nuisance variability in the data that more sophisticated methods struggle to learn away.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span 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href="/paper/Dual-Path-Learning-for-Domain-Adaptation-of-Cheng-Wei/eeb319597ba2e6792f2110c00946ffcb975c5882"><h3 id="paper-eeb319597ba2e6792f2110c00946ffcb975c5882" class="cl-paper-title" tabindex="" aria-describedby="">Dual Path Learning for Domain Adaptation of Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="2117233845" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Yiting-Cheng/2117233845"><span class=""><span>Yiting Cheng</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2480483" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Fangyun-Wei/2480483"><span class=""><span>Fangyun Wei</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="9324504" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Jianmin-Bao/9324504"><span class=""><span>Jianmin Bao</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="47514557" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Dong-Chen/47514557"><span class=""><span>Dong Chen</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="1716835" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Fang-Wen/1716835"><span class=""><span>Fang Wen</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2108126306" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Wenqiang-Zhang/2108126306"><span class=""><span>Wenqiang Zhang</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=IEEE%20International%20Conference%20on%20Computer%20Vision">IEEE International Conference on Computer Vision</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2021</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>This paper proposes a novel dual path learning (DPL) framework that contains two complementary and interactive single-domain adaptation pipelines aligned in source and target domain respectively and shows superiority of the DPL model over the state-of-the-art methods.</span></span><button aria-label="Expand truncated text" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default more mod-clickable more-toggle"><span class="cl-button__label">Expand</span></button></div><div class="cl-paper-card__controls"><div 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cl-button--has-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-tertiary cl-button--density-default cl-icon-button cl-paper-flag__flag-button"><span class="cl-button__icon"><svg aria-hidden="true" height="11" width="11" alt="" class="icon-svg icon-excerpt" data-test-id="icon-excerpt"><use xlink:href="#excerpt"/></svg></span><span class="cl-button__label">1 Excerpt</span></button></div></div></div><div class="cl-paper-flags__context"/></div></div><div class="cl-paper-card__content"><a class="link-button--show-visited" data-heap-id="citation_title" data-heap-paper-id="02e80059549497b13089cf943541c9e5a5f765a7" data-heap-citation-type="citedPapers" data-heap-has-intents="true" data-test-id="citation-paper-title" href="/paper/Context-Aware-Mixup-for-Domain-Adaptive-Semantic-Zhou-Feng/02e80059549497b13089cf943541c9e5a5f765a7"><h3 id="paper-02e80059549497b13089cf943541c9e5a5f765a7" class="cl-paper-title" tabindex="" aria-describedby="">Context-Aware Mixup for Domain Adaptive Semantic Segmentation</h3></a><ul class=""><span class="cl-paper-authors"><span data-heap-id="citation_author" data-heap-author-id="67190665" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Qianyu-Zhou/67190665"><span class=""><span>Qianyu Zhou</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="101592953" data-test-id="author-list"><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Zhengyang-Feng/101592953"><span class=""><span>Zhengyang Feng</span></span></a></span><span data-heap-id="citation_author" data-heap-author-id="2149343311" data-test-id="author-list"><span><button data-test-id="author-list-expand" aria-expanded="false" aria-label="Show 5 more authors" type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default cl-paper-authors__toggle cl-paper-authors__toggle__with-background"><span class="cl-button__label">+5 authors</span></button> </span><a class="cl-paper-authors__author-box notranslate cl-paper-authors__author-box__with-background" href="/author/Lizhuang-Ma/2149343311"><span class=""><span>Lizhuang Ma</span></span></a></span></span><li class="cl-paper__bulleted-row"><div class="cl-paper__bulleted-row__item"><span class="cl-paper-fos">Computer Science</span></div><div class="cl-paper__bulleted-row__item"><a class="cl-paper-venue cl-paper-venue--paper-metadata" data-heap-id="paper-meta-venue" href="/venue?name=IEEE%20transactions%20on%20circuits%20and%20systems%20for%20video%20technology%20%28Print%29">IEEE transactions on circuits and systems for…</a></div></li><li class="cl-paper__bulleted-row__item"><span class="cl-paper-pubdates"><span>2023</span></span></li></ul><div class="tldr-abstract-replacement text-truncator tldr__paper-card"><div class="cl-dropdown cl-dropdown--arrow-pos-top-left tldr-abstract__pill"><div class="cl-dropdown__button"><button type="submit" class="cl-button cl-button--no-arrow-divider cl-button--not-icon-only cl-button--no-icon cl-button--has-label cl-button--font-size- cl-button--icon-pos-left cl-button--shape-rectangle cl-button--size-default cl-button--type-default cl-button--density-default tldr-abstract-replacement__pill mod-clickable"><span class="cl-button__label">TLDR</span></button></div><div class="cl-dropdown__popover"/></div><span class=""><span>A novel Context-Aware Mixup (CAMix) framework for domain adaptive semantic segmentation, which exploits this important clue of context-dependency as explicit prior knowledge in a fully end-to-end trainable manner for enhancing the adaptability 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