{"id":13889,"date":"2025-04-10T12:35:35","date_gmt":"2025-04-10T17:35:35","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/?p=13889"},"modified":"2026-04-15T08:34:51","modified_gmt":"2026-04-15T12:34:51","slug":"interactive-segmentation-with-prototype-learning-for-few-shot-root-annotation","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2025\/04\/10\/interactive-segmentation-with-prototype-learning-for-few-shot-root-annotation\/","title":{"rendered":"Interactive Segmentation with Prototype Learning for Few-Shot Root Annotation"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Fine-scale pixel-level annotation of minirhizotron root images is a less common and challenging task. We present an interactive segmentation framework to accelerate root annotation. We leverage the concept of few-shot segmentation so that the pre-trained model can be effectively fine-tuned and transferred to an unseen category. To provide immediate feedback for real-time interaction, we adapted a UNet architecture by attaching lightweight embedding layers which leveraged a prototype learning approach to efficiently learn the data metric in the embedding space. The prototypes optimized by the prototype loss preserve the within-class data variation, enabling effective fine-tuning. Furthermore, we designed a system with our interactive annotation framework, and conducted an experiment with real users to validate the approach.<\/p>\n<h2>Links:<\/h2>\n<p><a href=\"https:\/\/ieeexplore.ieee.org\/document\/10947169\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-470 size-full\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/arxiv.png\" alt=\"IEEE Interactive Segmentation With Prototype Learning for Few-Shot Root Annotation\" width=\"90\" height=\"90\" \/><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>X. Guo, A. Zare, L. Anthony, and F. B. Fritschi, \"Interactive segmentation with prototype learning for few-shot root annotation,\" IEEE Transactions on Geoscience and Remote Sensing, pp. 1\u20131, 2025, doi: 10.1109\/TGRS.2025.3556799.<\/code><\/pre>\n<pre class=\"verbatim select-on-click\" title=\"click to copy to clipboard\"><code>@article{guo2025interactive,\ntitle={Interactive Segmentation with Prototype Learning for Few-Shot Root Annotation},\nauthor={Guo, Xiaolei and Zare, Alina and Anthony, Lisa and Fritschi, Felix B},\njournal={IEEE Transactions on Geoscience and Remote Sensing},\nyear={2025},\npublisher={IEEE}\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Fine-scale pixel-level annotation of minirhizotron root images is a less common and challenging task. We present an interactive segmentation framework to accelerate root annotation. We leverage the concept of few-shot segmentation so that the pre-trained model can be effectively fine-tuned and transferred to an unseen category. To provide immediate feedback for real-time interaction, we [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"off","footnotes":"","_links_to":"","_links_to_target":""},"categories":[19,13],"tags":[57,83,167,207,299,307,371,599,613,751],"class_list":["post-13889","post","type-post","status-publish","format-standard","hentry","category-journal_paper","category-publication","tag-adaptation-models","tag-annotations","tag-computational-modeling","tag-data-models","tag-feature-extraction","tag-few-shot","tag-image-segmentation","tag-prototypes","tag-real-time-systems","tag-transfer-learning"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/13889","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=13889"}],"version-history":[{"count":2,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/13889\/revisions"}],"predecessor-version":[{"id":16577,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/13889\/revisions\/16577"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=13889"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=13889"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=13889"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}