{"id":13642,"date":"2024-07-31T09:28:08","date_gmt":"2024-07-31T09:28:08","guid":{"rendered":"https:\/\/dailyai.com\/?p=13642"},"modified":"2024-07-31T17:50:03","modified_gmt":"2024-07-31T17:50:03","slug":"metas-sam-2-model-enables-accurate-video-segmentation-in-seconds","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/","title":{"rendered":"Metas SAM 2-model muligg\u00f8r n\u00f8jagtig videosegmentering p\u00e5 f\u00e5 sekunder"},"content":{"rendered":"

Metas forskningsafdeling har introduceret SAM 2 (Segment Anything Model 2), et AI-system, der markerer et stort fremskridt inden for videoanalyse.<\/b><\/p>\n

Denne nye model udvider sin forg\u00e6nger SAM's billedsegmenteringsevner og vover sig ud i det mere komplekse videodom\u00e6ne.<\/span><\/p>\n

Videosegmentering - evnen til at identificere og spore specifikke objekter i en scene i bev\u00e6gelse - har l\u00e6nge v\u00e6ret en udfordring for AI.\u00a0<\/span><\/p>\n

Mens mennesker ubesv\u00e6ret kan f\u00f8lge en bil, der bev\u00e6ger sig gennem trafikken, eller en person, der g\u00e5r gennem en menneskem\u00e6ngde, har AI-systemer en tendens til at k\u00e6mpe.\u00a0<\/span>Det er et k\u00e6mpe problem for f\u00f8rerl\u00f8se biler<\/a> og andre autonome k\u00f8ret\u00f8jer (AV'er), som har brug for at spore bev\u00e6gelige 3D-objekter i deres omgivelser.<\/p>\n

SAM 2<\/span><\/a> har til form\u00e5l at bygge bro over denne kl\u00f8ft og bringe AI's forst\u00e5else af video t\u00e6ttere p\u00e5 den menneskelige opfattelse.<\/span><\/p>\n

Systemet kan identificere og spore stort set ethvert objekt i en video med minimal brugerinput - nogle gange s\u00e5 lidt som et enkelt klik. <\/span>Det \u00e5bner op for en verden af muligheder inden for alt fra filmredigering til videnskabelig forskning.<\/span><\/p>\n

S\u00e5dan skabte og testede Meta SAM 2:<\/span><\/p>\n

    \n
  1. Teamet skabte en teknik kaldet Promptable Visual Segmentation (PVS), som giver brugerne mulighed for at guide den kunstige intelligens med enkle stikord p\u00e5 ethvert videobillede. Det betyder, at systemet kan tilpasse sig en lang r\u00e6kke scenarier, lige fra at spore en bestemt person i en menneskem\u00e6ngde til at f\u00f8lge bev\u00e6gelsen af en fugls vinge under flyvning.<\/span><\/li>\n
  2. De byggede en modelarkitektur, der omfattede komponenter til behandling af individuelle billeder, lagring af oplysninger om objekter over tid og generering af pr\u00e6cise segmenteringer. <\/span><\/li>\n
  3. Et n\u00f8gleelement er hukommelsesmodulet, som g\u00f8r det muligt for SAM 2 at opretholde konsekvent sporing, selv n\u00e5r objekter midlertidigt forsvinder ud af syne.<\/span><\/li>\n
  4. Der blev skabt et massivt nyt datas\u00e6t med over 50.000 videoer og 35 millioner m\u00e6rkede billeder, som overg\u00e5r tidligere datas\u00e6t med videosegmentering. T<\/span><\/li>\n
  5. Dette datas\u00e6t, kaldet SA-V, d\u00e6kker et bredt spektrum af objekttyper, st\u00f8rrelser og scenarier, hvilket forbedrer modellens evne til at generalisere til nye situationer.<\/span><\/li>\n
  6. Modellen gennemgik omfattende tr\u00e6ning og test p\u00e5 tv\u00e6rs af 17 forskellige videodatas\u00e6t, fra bilkameraoptagelser til medicinsk billedbehandling. <\/span><\/li>\n
  7. SAM 2 overgik eksisterende state-of-the-art-metoder i semi-overv\u00e5gede videoobjektsegmenteringsopgaver og opn\u00e5ede en gennemsnitlig forbedring p\u00e5 7,5% i J&F-score (en standardm\u00e5ling for segmenteringskvalitet).<\/span><\/li>\n<\/ol>\n
    https:\/\/dailyai.com\/wp-content\/uploads\/2024\/07\/An9rqhh8jLAD81wdPLApEcu-1Z2NzXx4_528PrvelbMmWVgTvtk8DgdwDHRYohgxxotkX_WENwyB-eegqi3EQXlQ.mp4<\/a><\/video><\/div>\n

    Ovenfor<\/strong>: Billedsegmentering til komplekse videoklip adskiller forskellige former p\u00e5 f\u00e5 sekunder.<\/p>\n

      \n
    • I filmproduktion kan SAM 2 effektivisere arbejdet med visuelle effekter og spare tid i efterproduktionen.<\/li>\n
    • Forskere kan spore celler i mikroskopi-optagelser eller overv\u00e5ge milj\u00f8forandringer i satellitbilleder<\/li>\n
    • For AV'er, herunder f\u00f8rerl\u00f8se biler, kan SAM 2 forbedre objektregistrering i komplekse trafikscenarier<\/li>\n
    • Dyrev\u00e6rnsfolk kan bruge SAM 2 til at overv\u00e5ge dyrebestande i store omr\u00e5der<\/li>\n
    • I AR\/VR kan det muligg\u00f8re mere pr\u00e6cise interaktioner med virtuelle objekter i live video<\/li>\n<\/ul>\n

      I overensstemmelse med Metas engagement i \u00e5ben forskning udgives SAM 2 som open source-software.\u00a0<\/span><\/p>\n

      Det g\u00e6lder ikke kun modellen, men ogs\u00e5 det datas\u00e6t, der er brugt til at tr\u00e6ne den.\u00a0<\/span><\/p>\n

      Forskerne er allerede i gang med at unders\u00f8ge, hvordan man kan h\u00e5ndtere l\u00e6ngere videoer, forbedre ydeevnen p\u00e5 fine detaljer og reducere den computerkraft, der kr\u00e6ves for at k\u00f8re modellen.<\/span><\/p>\n

      Efterh\u00e5nden som billedsegmenteringsteknologien modnes, vil den helt sikkert \u00e6ndre den m\u00e5de, vi interagerer med og analyserer videoindhold p\u00e5. <\/span><\/p>\n

      Fra at g\u00f8re komplekse redigeringsopgaver mere tilg\u00e6ngelige til at muligg\u00f8re nye former for visuel analyse flytter SAM 2 gr\u00e6nserne for visuel manipulation.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

      Metas forskningsafdeling har introduceret SAM 2 (Segment Anything Model 2), et AI-system, der markerer et stort fremskridt inden for videoanalyse. Denne nye model udvider sin forg\u00e6nger SAM's billedsegmenteringsevner og vover sig ind p\u00e5 det mere komplekse videodom\u00e6ne. Videosegmentering - evnen til at identificere og spore specifikke objekter i en scene i bev\u00e6gelse - har l\u00e6nge v\u00e6ret en udfordring for AI. Mens mennesker uden problemer kan f\u00f8lge en bil, n\u00e5r den bev\u00e6ger sig gennem trafikken, eller en person, der g\u00e5r gennem en menneskem\u00e6ngde, har AI-systemer en tendens til at k\u00e6mpe. Det er et stort problem for f\u00f8rerl\u00f8se biler og andre autonome k\u00f8ret\u00f8jer (AV'er),<\/p>","protected":false},"author":2,"featured_media":13644,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[105,131],"class_list":["post-13642","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-machine-learning","tag-meta"],"yoast_head":"\nMeta's SAM 2 model enables accurate video segmentation in seconds | DailyAI<\/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:\/\/dailyai.com\/da\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Meta's SAM 2 model enables accurate video segmentation in seconds | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Meta’s research division has introduced SAM 2 (Segment Anything Model 2), an AI system that marks a huge advancement in video analysis. This new model expands on its predecessor SAM\u2019s image segmentation skills, venturing into the more complex domain of video. Video segmentation – the ability to identify and track specific objects in a moving scene – has long been a challenge for AI.\u00a0 While humans can effortlessly follow a car as it moves through traffic or a person walking through a crowd, AI systems tend to struggle.\u00a0This is a massive problem for driverless cars and other autonomous vehicles (AVs),\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/da\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-07-31T09:28:08+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-31T17:50:03+00:00\" \/>\n<meta property=\"og:image\" 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