{"id":2,"date":"2026-04-20T03:51:14","date_gmt":"2026-04-20T03:51:14","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf21\/?page_id=2"},"modified":"2026-05-06T18:20:23","modified_gmt":"2026-05-06T18:20:23","slug":"sample-page","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf21\/sample-page\/","title":{"rendered":"Related Work"},"content":{"rendered":"\n

One major challenge is lack of paired RGB-IR data.<\/p>\n\n\n\n

F-ViTA (Foundation Model Guided Visible-to-Infrared Translation)<\/strong><\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n

F-ViTA generates thermal (IR) images from RGB inputs using object-level and scene-level conditioning guided by foundation models. This allows models to learn realistic heat patterns and improves performance in settings where paired RGB-IR data is limited.<\/p>\n\n\n\n

<\/p>\n\n\n\n

TherA (Thermal-Aware Visual-Language Prompting)<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

TherA enhances RGB-to-thermal translation by incorporating temperature-aware prompts, enabling more controllable and semantically meaningful thermal image generation. It improves how well generated IR images reflect real-world thermal cues, making them more useful for downstream tasks.<\/p>\n\n\n\n

<\/p>\n\n\n\n

RARL (Reasoning-Aware Reinforcement Learning with LoRA)<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

RARL improves vision-language models by encouraging structured reasoning through reinforcement learning with multiple reward signals, including accuracy and reasoning quality. Combined with LoRA for efficient fine-tuning, it enables better generalization and transparent decision-making under limited data and compute constraints.
<\/p>\n","protected":false},"excerpt":{"rendered":"

One major challenge is lack of paired RGB-IR data. F-ViTA (Foundation Model Guided Visible-to-Infrared Translation) F-ViTA generates thermal (IR) images from RGB inputs using object-level and scene-level conditioning guided by foundation models. This allows models to learn realistic heat patterns and improves performance in settings where paired RGB-IR data is limited. TherA (Thermal-Aware Visual-Language Prompting) … <\/p>\n

Continue reading “Related Work”<\/span><\/a><\/p>\n","protected":false},"author":264,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"yoast_head":"\nRelated Work - Multimodal Medical Triaging on UAV Data for DARPA Triage Challenge<\/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\/2026teamf21\/sample-page\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Related Work - Multimodal Medical Triaging on UAV Data for DARPA Triage Challenge\" \/>\n<meta property=\"og:description\" content=\"One major challenge is lack of paired RGB-IR data. 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