{"id":13,"date":"2026-05-02T00:12:34","date_gmt":"2026-05-02T00:12:34","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/?page_id=13"},"modified":"2026-05-06T03:48:19","modified_gmt":"2026-05-06T03:48:19","slug":"related-work","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/related-work\/","title":{"rendered":"Related Work"},"content":{"rendered":"\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-6c531013 wp-block-group-is-layout-flex\">\n<p class=\"has-x-large-font-size\"><strong>TenebrioVision<\/strong><\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/vcl.iti.gr\/media\/documents\/tenebriovision-a-fully-annotated-dataset-of-tenebrio-molitor-larvae-worms-in-a-controlled-environment-for-accurate-small-object-detection-and-segmentation.pdf\">Link<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"362\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/tenebriovision-1024x362.jpg\" alt=\"\" class=\"wp-image-73\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/tenebriovision-1024x362.jpg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/tenebriovision-300x106.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/tenebriovision-768x272.jpg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/tenebriovision-1536x543.jpg 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/tenebriovision-2048x725.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>Tenebrio molitor<\/strong>\u00a0(mealworm) is a promising sustainable source of energy and protein for human consumption. This dataset offers a significant advancement over previous work, providing a high-quality and large-scale dataset for insect-based computer vision tasks.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>High-Density Annotation:<\/strong>&nbsp;Features 10\u2013100 annotations per image, totaling over&nbsp;<strong>53,600+ instances<\/strong>&nbsp;with precise bounding boxes and instance segmentation masks.<\/li>\n\n\n\n<li><strong>Superior Resolution:<\/strong>&nbsp;High-fidelity frames captured at&nbsp;<strong>3088 x 2076 pixels<\/strong>&nbsp;for fine-grained feature extraction.<\/li>\n\n\n\n<li><strong>Temporal Diversity:<\/strong>&nbsp;Curated from video sequences using&nbsp;<strong>30-second interval sampling<\/strong>&nbsp;to ensure visual variety and reduce data redundancy.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-6c531013 wp-block-group-is-layout-flex\">\n<p class=\"has-x-large-font-size\"><strong>SAM 3<\/strong><\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ai.meta.com\/research\/sam3\/\">Link<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"311\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/sam3-1024x311.png\" alt=\"\" class=\"wp-image-78\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/sam3-1024x311.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/sam3-300x91.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/sam3-768x233.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/sam3-1536x466.png 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/wp-content\/uploads\/sites\/157\/2026\/05\/sam3.png 1904w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Building upon the success of its predecessors,&nbsp;<strong>SAM 3<\/strong>&nbsp;represents a major leap in foundation models for segmentation. It addresses the fundamental limitations of static and video-based segmentation through several key innovations:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Unified Detection &amp; Tracking (DETR-style):<\/strong>\u00a0SAM 3 introduces a novel\u00a0<strong>DETR-style architecture<\/strong>\u00a0that\u00a0<strong>disentangles<\/strong>\u00a0the operational conflict between tracking established targets and discovering new objects. By unifying these predictions, the model maintains high temporal consistency while remaining sensitive to newly appearing instances in complex environments.<\/li>\n\n\n\n<li><strong>Massive Scale-up with Semantic Concepts:<\/strong>\u00a0The training data has evolved from the class-agnostic masks of SAM 2 to the\u00a0<strong>SA-Co dataset<\/strong>, which includes\u00a0<strong>1.4 billion masks<\/strong>\u00a0across\u00a0<strong>4 million unique noun phrases<\/strong>. This enables true open-vocabulary segmentation based on a 22.4-million-node knowledge ontology.<\/li>\n\n\n\n<li><strong>Zero-shot Generalization:<\/strong>\u00a0With its refined architecture, SAM 3 demonstrates superior\u00a0<strong>zero-shot ability<\/strong>, allowing it to segment unseen categories (such as specific life stages of\u00a0<em>Tenebrio molitor<\/em>) with unprecedented precision.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-x-large-font-size\"><\/p>\n\n\n\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-6c531013 wp-block-group-is-layout-flex\">\n<p class=\"has-x-large-font-size\"><strong><strong>DeepLabCut<\/strong><\/strong><\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/deeplabcut.github.io\/DeepLabCut\/README.html\">Link<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/camo.githubusercontent.com\/c6fb5040d3a7aa5a704c93df08f38a0abafab52e95894942ea4535655b809cb4\/68747470733a2f2f696d616765732e73717561726573706163652d63646e2e636f6d2f636f6e74656e742f76312f3537663664353163396637343536366635356563663237312f313632383235303030343232392d4b565944374a4a5648594546444a33324c39564a2f444c436c6f676f323032312e6a70673f666f726d61743d3130303077\" alt=\"\" \/><\/figure>\n\n\n\n<p>DeepLabCut has a well-established lineage and saw early adoption from the neuroscience community. The model evolved from the earlier human-centric pose estimation models DeepCut and DeeperCut. Although the original paper was published in 2018, DeepLabCut has continued to grow into a robust toolkit for a variety of non-human animal pose estimation.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Markerless pose estimation removed the need to attach physical markers to animals, which limited the skeleton that could be detected and involved substantial manual set-up<\/li>\n\n\n\n<li>Based on ResNet architecture pretrained on ImageNet with deconvolutional layers to maintain pixel-level predictions<\/li>\n\n\n\n<li>Many ablations: \n<ul class=\"wp-block-list\">\n<li>Training on single animal images and predicting on multi-animal images to evaluate generalization<\/li>\n\n\n\n<li>Varying the number of transfer learning training images to evaluate the data requirements<\/li>\n\n\n\n<li>Training with a subset vs. all body parts to evaluate the value of additional joint context<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Recent work with foundation models have produced &#8220;SuperAnimal&#8221; models that exhibit strong zero-shot performance on 40+ quadrupeds (e.g. dogs, cats, elephants, giraffes)<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>TenebrioVision Tenebrio molitor\u00a0(mealworm) is a promising sustainable source of energy and protein for human consumption. This dataset offers a significant advancement over previous work, providing a high-quality and large-scale dataset for insect-based computer vision tasks. SAM 3 Building upon the success of its predecessors,&nbsp;SAM 3&nbsp;represents a major leap in foundation models for segmentation. It addresses [&hellip;]<\/p>\n","protected":false},"author":297,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-13","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Related Work - Bioreactor Digital Twin<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf15\/related-work\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Related Work - Bioreactor Digital Twin\" \/>\n<meta property=\"og:description\" content=\"TenebrioVision Tenebrio molitor\u00a0(mealworm) is a promising sustainable source of energy and protein for human consumption. This dataset offers a significant advancement over previous work, providing a high-quality and large-scale dataset for insect-based computer vision tasks. SAM 3 Building upon the success of its predecessors,&nbsp;SAM 3&nbsp;represents a major leap in foundation models for segmentation. 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