{"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\/nb\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/","title":{"rendered":"Metas SAM 2-modell muliggj\u00f8r n\u00f8yaktig videosegmentering p\u00e5 f\u00e5 sekunder"},"content":{"rendered":"

Metas forskningsavdeling har introdusert SAM 2 (Segment Anything Model 2), et AI-system som markerer et stort fremskritt innen videoanalyse.<\/b><\/p>\n

Denne nye modellen utvider forgjengeren SAMs ferdigheter innen bildesegmentering, og v\u00e5ger seg inn p\u00e5 det mer komplekse videodomenet.<\/span><\/p>\n

Videosegmentering - evnen til \u00e5 identifisere og spore spesifikke objekter i en scene i bevegelse - har lenge v\u00e6rt en utfordring for kunstig intelligens.\u00a0<\/span><\/p>\n

Mens mennesker uten problemer kan f\u00f8lge en bil i trafikken eller en person som g\u00e5r gjennom en folkemengde, har AI-systemer en tendens til \u00e5 slite.\u00a0<\/span>Dette er et stort problem for f\u00f8rerl\u00f8se biler<\/a> og andre autonome kj\u00f8ret\u00f8y (AV), som har behov for \u00e5 spore bevegelige 3D-objekter i omgivelsene sine.<\/p>\n

SAM 2<\/span><\/a> M\u00e5let er \u00e5 bygge bro over dette gapet og bringe AIs forst\u00e5else av video n\u00e6rmere menneskelig persepsjon.<\/span><\/p>\n

Systemet kan identifisere og spore praktisk talt alle objekter i en video med minimal brukerinput - noen ganger s\u00e5 lite som et enkelt klikk. <\/span>Dette \u00e5pner for en verden av muligheter innen alt fra filmredigering til vitenskapelig forskning.<\/span><\/p>\n

Slik opprettet og testet Meta SAM 2:<\/span><\/p>\n

    \n
  1. Teamet utviklet en teknikk kalt Promptable Visual Segmentation (PVS), som gj\u00f8r det mulig for brukerne \u00e5 veilede den kunstige intelligensen med enkle ledetr\u00e5der i alle videobilder. Det betyr at systemet kan tilpasse seg et bredt spekter av scenarier, fra \u00e5 spore en bestemt person i en folkemengde til \u00e5 f\u00f8lge bevegelsene til en fugl i flukt.<\/span><\/li>\n
  2. De bygget en modellarkitektur som inkluderte komponenter for behandling av individuelle bilder, lagring av informasjon om objekter over tid og generering av presise segmenteringer. <\/span><\/li>\n
  3. Et n\u00f8kkelelement er minnemodulen, som gj\u00f8r at SAM 2 kan opprettholde konsekvent sporing selv n\u00e5r objekter midlertidig forsvinner ut av syne.<\/span><\/li>\n
  4. Det ble opprettet et massivt nytt datasett som inneholder over 50 000 videoer og 35 millioner merkede bilder, noe som overg\u00e5r tidligere datasett for videosegmentering. T<\/span><\/li>\n
  5. Dette datasettet, kalt SA-V, dekker et bredt spekter av objekttyper, st\u00f8rrelser og scenarier, noe som forbedrer modellens evne til \u00e5 generalisere til nye situasjoner.<\/span><\/li>\n
  6. Modellen gjennomgikk omfattende trening og testing p\u00e5 tvers av 17 ulike videodatasett, fra bilkameraopptak til medisinsk bildebehandling. <\/span><\/li>\n
  7. SAM 2 utkonkurrerte eksisterende toppmoderne metoder i semioppsynte segmenteringsoppgaver for videoobjekter, og oppn\u00e5dde en gjennomsnittlig forbedring p\u00e5 7,5% i J&F-score (et standardm\u00e5l 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>: Bildesegmentering for komplekse videoklipp skiller ut ulike former p\u00e5 f\u00e5 sekunder.<\/p>\n

      \n
    • I filmproduksjon kan SAM 2 effektivisere arbeidet med visuelle effekter og spare tid i etterproduksjonen<\/li>\n
    • Forskere kan spore celler i mikroskopibilder eller overv\u00e5ke milj\u00f8endringer i satellittbilder<\/li>\n
    • For AV-biler, inkludert f\u00f8rerl\u00f8se biler, kan SAM 2 forbedre objektdeteksjonen i komplekse trafikkscenarier<\/li>\n
    • Naturvernere kan bruke SAM 2 til \u00e5 overv\u00e5ke dyrebestander i store omr\u00e5der<\/li>\n
    • I AR\/VR kan det muliggj\u00f8re mer n\u00f8yaktig interaksjon med virtuelle objekter i live video<\/li>\n<\/ul>\n

      I tr\u00e5d med Metas forpliktelse til \u00e5pen forskning lanseres SAM 2 som programvare med \u00e5pen kildekode.\u00a0<\/span><\/p>\n

      Dette omfatter ikke bare modellen, men ogs\u00e5 datasettet som brukes til \u00e5 trene den.\u00a0<\/span><\/p>\n

      Forskerne er allerede i gang med \u00e5 utforske m\u00e5ter \u00e5 h\u00e5ndtere lengre videoer p\u00e5, forbedre ytelsen p\u00e5 fine detaljer og redusere beregningskraften som kreves for \u00e5 kj\u00f8re modellen.<\/span><\/p>\n

      Etter hvert som bildesegmenteringsteknologien modnes, vil den helt sikkert forandre m\u00e5ten vi samhandler med og analyserer videoinnhold p\u00e5. <\/span><\/p>\n

      SAM 2 flytter grensene for visuell manipulering, fra \u00e5 gj\u00f8re komplekse redigeringsoppgaver mer tilgjengelige til \u00e5 muliggj\u00f8re nye former for visuell analyse.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

      Metas forskningsavdeling har introdusert SAM 2 (Segment Anything Model 2), et AI-system som markerer et stort fremskritt innen videoanalyse. Denne nye modellen utvider forgjengeren SAMs bildesegmenteringsferdigheter, og v\u00e5ger seg inn i det mer komplekse videodomenet. Videosegmentering - evnen til \u00e5 identifisere og spore spesifikke objekter i en scene i bevegelse - har lenge v\u00e6rt en utfordring for kunstig intelligens. Mens mennesker uten problemer kan f\u00f8lge en bil i trafikken eller en person som g\u00e5r gjennom en folkemengde, har AI-systemer en tendens til \u00e5 slite. Dette er et stort problem for f\u00f8rerl\u00f8se biler og andre autonome kj\u00f8ret\u00f8y (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\/nb\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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\/nb\/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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