{"id":10840,"date":"2022-06-21T07:28:04","date_gmt":"2022-06-21T12:28:04","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/?p=10840"},"modified":"2026-04-13T17:34:21","modified_gmt":"2026-04-13T21:34:21","slug":"bag-level-classification-network-for-infrared-target-detection","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2022\/06\/21\/bag-level-classification-network-for-infrared-target-detection\/","title":{"rendered":"Bag-level Classification Network for Infrared Target Detection"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Aided target detection in infrared data has proven an important area of investigation for both military and civilian applications. While target detection at the object or pixel-level has been explored extensively, existing approaches require precisely-annotated data which is often expensive or difficult to obtain. Leveraging advancements in weakly supervised semantic segmentation, this paper explores the feasibility of learning a pixel-level classification scheme given only image-level label information. Specifically, we investigate the use of class activation maps to inform feature selection for binary, pixel-level classification tasks. Results are given on four infrared aided target recognition datasets of varying difficulty. Results are quantitatively evaluated using common approaches in the literature.<\/p>\n<h2>Links:<\/h2>\n<p><a href=\"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12096\/1209603\/Bag-level-classification-network-for-infrared-target-detection\/10.1117\/12.2618325.full?SSO=1\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-454 size-full\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/SPIE-logo-cmyk-e1482256584489.jpg\" alt=\"SPIE Bag-level classification network for infrared target detection\" width=\"296\" height=\"90\" \/><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre class=\"verbatim select-on-click\" title=\"click to copy to clipboard\"><code>Connor H. McCurley, Daniel Rodriguez, Chandler Trousdale, Arielle Stevens, Anthony Baldino, Eugene Li, Isabella Perlmutter, and Alina Zare \"Bag-level classification network for infrared target detection\", Proc. SPIE 12096, Automatic Target Recognition XXXII, 1209603, 2022; https:\/\/doi.org\/10.1117\/12.2618325<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Aided target detection in infrared data has proven an important area of investigation for both military and civilian applications. While target detection at the object or pixel-level has been explored extensively, existing approaches require precisely-annotated data which is often expensive or difficult to obtain. Leveraging advancements in weakly supervised semantic segmentation, this paper explores [&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":[9,13],"tags":[149,303,377,381,489,733,791,795],"class_list":["post-10840","post","type-post","status-publish","format-standard","hentry","category-news","category-publication","tag-class-activation-map","tag-feature-selection","tag-imprecise-labels","tag-infrared","tag-multiple-instance-learning","tag-target-detection","tag-weak-learning","tag-weakly-supervised-semantic-segmentation"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/10840","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=10840"}],"version-history":[{"count":2,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/10840\/revisions"}],"predecessor-version":[{"id":16509,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/10840\/revisions\/16509"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=10840"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=10840"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=10840"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}