{"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\/sv\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/","title":{"rendered":"Metas SAM 2-modell m\u00f6jligg\u00f6r exakt videosegmentering p\u00e5 n\u00e5gra sekunder"},"content":{"rendered":"

Metas forskningsavdelning har introducerat SAM 2 (Segment Anything Model 2), ett AI-system som markerar ett stort framsteg inom videoanalys.<\/b><\/p>\n

Den h\u00e4r nya modellen bygger vidare p\u00e5 f\u00f6reg\u00e5ngaren SAM:s kunskaper om bildsegmentering och ger sig in p\u00e5 den mer komplexa videodom\u00e4nen.<\/span><\/p>\n

Videosegmentering - f\u00f6rm\u00e5gan att identifiera och sp\u00e5ra specifika objekt i en r\u00f6rlig scen - har l\u00e4nge varit en utmaning f\u00f6r AI.\u00a0<\/span><\/p>\n

Medan m\u00e4nniskor utan problem kan f\u00f6lja en bil som r\u00f6r sig genom trafiken eller en person som g\u00e5r genom en folkmassa, tenderar AI-system att ha det sv\u00e5rare.\u00a0<\/span>Detta \u00e4r ett stort problem f\u00f6r F\u00f6rarl\u00f6sa bilar<\/a> och andra autonoma fordon (AV), som beh\u00f6ver sp\u00e5ra r\u00f6rliga 3D-objekt i sin omgivning.<\/p>\n

SAM 2<\/span><\/a> syftar till att \u00f6verbrygga denna klyfta och f\u00f6ra AI:s f\u00f6rst\u00e5else av video n\u00e4rmare den m\u00e4nskliga niv\u00e5n.<\/span><\/p>\n

Systemet kan identifiera och sp\u00e5ra praktiskt taget vilket objekt som helst i en video med minimala anv\u00e4ndarinst\u00e4llningar - ibland s\u00e5 lite som ett enda klick. <\/span>Detta \u00f6ppnar en v\u00e4rld av m\u00f6jligheter inom omr\u00e5den som str\u00e4cker sig fr\u00e5n filmredigering till vetenskaplig forskning.<\/span><\/p>\n

H\u00e4r \u00e4r hur Meta skapade och testade SAM 2:<\/span><\/p>\n

    \n
  1. Teamet skapade en teknik som kallas Promptable Visual Segmentation (PVS), som g\u00f6r det m\u00f6jligt f\u00f6r anv\u00e4ndare att v\u00e4gleda AI med enkla ledtr\u00e5dar p\u00e5 valfri videobild. Detta inneb\u00e4r att systemet kan anpassas till en m\u00e4ngd olika scenarier, fr\u00e5n att sp\u00e5ra en specifik person i en folkmassa till att f\u00f6lja en f\u00e5gelvinges r\u00f6relse under flygning.<\/span><\/li>\n
  2. De byggde en modellarkitektur som inneh\u00f6ll komponenter f\u00f6r bearbetning av enskilda bildrutor, lagring av information om objekt \u00f6ver tid och generering av exakta segmenteringar. <\/span><\/li>\n
  3. Ett viktigt element \u00e4r minnesmodulen, som g\u00f6r att SAM 2 kan uppr\u00e4tth\u00e5lla en konsekvent sp\u00e5rning \u00e4ven n\u00e4r objekt tillf\u00e4lligt f\u00f6rsvinner ur sikte.<\/span><\/li>\n
  4. En massiv ny dataset skapades, som inneh\u00e5ller \u00f6ver 50.000 videor och 35 miljoner m\u00e4rkta bildrutor, vilket \u00f6vertr\u00e4ffar tidigare dataset f\u00f6r videosegmentering. T<\/span><\/li>\n
  5. Datam\u00e4ngden, som kallas SA-V, omfattar ett brett spektrum av objekttyper, storlekar och scenarier, vilket f\u00f6rb\u00e4ttrar modellens f\u00f6rm\u00e5ga att generalisera till nya situationer.<\/span><\/li>\n
  6. Modellen genomgick omfattande tr\u00e4ning och testning i 17 olika videodataset, fr\u00e5n bilkamerafilmer till medicinsk bildbehandling. <\/span><\/li>\n
  7. SAM 2 \u00f6vertr\u00e4ffade befintliga state-of-the-art-metoder i semi-\u00f6vervakade segmenteringsuppgifter f\u00f6r videoobjekt och uppn\u00e5dde en genomsnittlig f\u00f6rb\u00e4ttring p\u00e5 7,5% i J&F-po\u00e4ng (ett standardm\u00e5tt f\u00f6r segmenteringskvalitet).<\/span><\/li>\n<\/ol>\n
    https:\/\/dailyai.com\/wp-content\/uploads\/2024\/07\/An9rqhh8jLAD81wdPLApEcu-1Z2NzXx4_528PrvelbMmWVgTvtk8DgdwDHRYohgxxotkX_WENwyB-eegqi3EQXlQ.mp4<\/a><\/video><\/div>\n

    Ovan<\/strong>: Bildsegmentering f\u00f6r komplexa videoklipp separerar olika former p\u00e5 n\u00e5gra sekunder.<\/p>\n

      \n
    • Inom filmproduktion kan SAM 2 effektivisera arbetet med visuella effekter och spara tid i efterproduktionen<\/li>\n
    • Forskare kan sp\u00e5ra celler i mikroskopiska bilder eller \u00f6vervaka milj\u00f6f\u00f6r\u00e4ndringar i satellitbilder<\/li>\n
    • F\u00f6r AV-fordon, inklusive f\u00f6rarl\u00f6sa bilar, kan SAM 2 f\u00f6rb\u00e4ttra objektdetekteringen i komplexa trafiksituationer<\/li>\n
    • Naturv\u00e5rdare kan anv\u00e4nda SAM 2 f\u00f6r att \u00f6vervaka djurpopulationer i stora omr\u00e5den<\/li>\n
    • Inom AR\/VR kan det m\u00f6jligg\u00f6ra mer exakta interaktioner med virtuella objekt i livevideo<\/li>\n<\/ul>\n

      I enlighet med Metas engagemang f\u00f6r \u00f6ppen forskning sl\u00e4pps SAM 2 som programvara med \u00f6ppen k\u00e4llkod.\u00a0<\/span><\/p>\n

      Detta omfattar inte bara modellen utan \u00e4ven det dataset som anv\u00e4ndes f\u00f6r att tr\u00e4na den.\u00a0<\/span><\/p>\n

      Forskarna h\u00e5ller redan p\u00e5 att utforska olika s\u00e4tt att hantera l\u00e4ngre videor, f\u00f6rb\u00e4ttra prestandan f\u00f6r fina detaljer och minska den ber\u00e4kningskraft som kr\u00e4vs f\u00f6r att k\u00f6ra modellen.<\/span><\/p>\n

      I takt med att tekniken f\u00f6r bildsegmentering utvecklas kommer den s\u00e4kerligen att f\u00f6r\u00e4ndra hur vi interagerar med och analyserar videoinneh\u00e5ll. <\/span><\/p>\n

      Fr\u00e5n att g\u00f6ra komplexa redigeringsuppgifter mer tillg\u00e4ngliga till att m\u00f6jligg\u00f6ra nya former av visuell analys, flyttar SAM 2 fram gr\u00e4nserna f\u00f6r visuell manipulation.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

      Metas forskningsavdelning har introducerat SAM 2 (Segment Anything Model 2), ett AI-system som markerar ett stort framsteg inom videoanalys. Denna nya modell expanderar p\u00e5 sin f\u00f6reg\u00e5ngare SAMs bildsegmenteringsf\u00f6rm\u00e5ga och v\u00e5gar sig in i den mer komplexa dom\u00e4nen f\u00f6r video. Videosegmentering - f\u00f6rm\u00e5gan att identifiera och sp\u00e5ra specifika objekt i en r\u00f6rlig scen - har l\u00e4nge varit en utmaning f\u00f6r AI. Medan m\u00e4nniskor utan problem kan f\u00f6lja en bil n\u00e4r den r\u00f6r sig genom trafiken eller en person som g\u00e5r genom en folkmassa, tenderar AI-system att k\u00e4mpa. Det h\u00e4r \u00e4r ett stort problem f\u00f6r f\u00f6rarl\u00f6sa bilar och andra autonoma fordon (AV),<\/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\/sv\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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\/sv\/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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