{"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\/de\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/","title":{"rendered":"Das SAM 2-Modell von Meta erm\u00f6glicht eine genaue Videosegmentierung in Sekundenschnelle"},"content":{"rendered":"

Die Forschungsabteilung von Meta hat SAM 2 (Segment Anything Model 2) vorgestellt, ein KI-System, das einen gro\u00dfen Fortschritt in der Videoanalyse darstellt.<\/b><\/p>\n

Dieses neue Modell erweitert die F\u00e4higkeiten seines Vorg\u00e4ngers SAM bei der Bildsegmentierung und wagt sich in den komplexeren Bereich der Videos.<\/span><\/p>\n

Die Segmentierung von Videos - die F\u00e4higkeit, bestimmte Objekte in einer sich bewegenden Szene zu identifizieren und zu verfolgen - ist seit langem eine Herausforderung f\u00fcr die KI.\u00a0<\/span><\/p>\n

W\u00e4hrend Menschen m\u00fchelos einem Auto im Stra\u00dfenverkehr oder einer Person, die durch eine Menschenmenge l\u00e4uft, folgen k\u00f6nnen, haben KI-Systeme in der Regel Schwierigkeiten.\u00a0<\/span>Dies ist ein massives Problem f\u00fcr fahrerlose Autos<\/a> und andere autonome Fahrzeuge (AVs), die sich bewegende 3D-Objekte in ihrer Umgebung verfolgen m\u00fcssen.<\/p>\n

SAM 2<\/span><\/a> zielt darauf ab, diese L\u00fccke zu schlie\u00dfen und das Verst\u00e4ndnis der KI f\u00fcr Videos n\u00e4her an die menschliche Wahrnehmung heranzuf\u00fchren.<\/span><\/p>\n

Das System kann praktisch jedes Objekt in einem Video mit minimalen Benutzereingaben identifizieren und verfolgen - manchmal gen\u00fcgt ein einziger Klick. <\/span>Dies er\u00f6ffnet eine Vielzahl von M\u00f6glichkeiten, die von der Filmbearbeitung bis zur wissenschaftlichen Forschung reichen.<\/span><\/p>\n

Hier erfahren Sie, wie Meta SAM 2 erstellt und getestet hat:<\/span><\/p>\n

    \n
  1. Das Team hat eine Technik namens Promptable Visual Segmentation (PVS) entwickelt, die es den Benutzern erm\u00f6glicht, die KI mit einfachen Hinweisen auf jedem Videobild zu steuern. Das bedeutet, dass sich das System an eine breite Palette von Szenarien anpassen kann, von der Verfolgung einer bestimmten Person in einer Menschenmenge bis hin zur Verfolgung der Fl\u00fcgelbewegung eines Vogels im Flug.<\/span><\/li>\n
  2. Sie bauten eine Modellarchitektur auf, die Komponenten f\u00fcr die Verarbeitung einzelner Bilder, die Speicherung von Informationen \u00fcber Objekte im Zeitverlauf und die Erstellung pr\u00e4ziser Segmentierungen umfasste. <\/span><\/li>\n
  3. Ein Schl\u00fcsselelement ist das Speichermodul, das es SAM 2 erm\u00f6glicht, eine konsistente Verfolgung beizubehalten, selbst wenn Objekte vor\u00fcbergehend aus dem Blickfeld verschwinden.<\/span><\/li>\n
  4. Es wurde ein riesiger neuer Datensatz mit \u00fcber 50.000 Videos und 35 Millionen beschrifteten Bildern erstellt, der fr\u00fchere Videosegmentierungsdatens\u00e4tze in den Schatten stellt. T<\/span><\/li>\n
  5. ieser Datensatz, SA-V genannt, deckt ein breites Spektrum von Objekttypen, -gr\u00f6\u00dfen und -szenarien ab und verbessert die F\u00e4higkeit des Modells, sich auf neue Situationen zu verallgemeinern.<\/span><\/li>\n
  6. Das Modell wurde in 17 verschiedenen Videodatens\u00e4tzen, von Dashcam-Aufnahmen bis hin zu medizinischen Bildern, ausgiebig trainiert und getestet. <\/span><\/li>\n
  7. SAM 2 \u00fcbertraf die existierenden State-of-the-Art-Methoden in halb\u00fcberwachten Videoobjekt-Segmentierungsaufgaben und erreichte eine durchschnittliche Verbesserung von 7,5% in den J&F-Scores (eine Standardmetrik f\u00fcr die Segmentierungsqualit\u00e4t).<\/span><\/li>\n<\/ol>\n
    https:\/\/dailyai.com\/wp-content\/uploads\/2024\/07\/An9rqhh8jLAD81wdPLApEcu-1Z2NzXx4_528PrvelbMmWVgTvtk8DgdwDHRYohgxxotkX_WENwyB-eegqi3EQXlQ.mp4<\/a><\/video><\/div>\n

    \u00dcber<\/strong>: Die Bildsegmentierung f\u00fcr komplexe Videoclips trennt verschiedene Formen in Sekundenschnelle.<\/p>\n

      \n
    • In der Filmproduktion k\u00f6nnte SAM 2 die Arbeit mit visuellen Effekten rationalisieren und Zeit in der Postproduktion sparen.<\/li>\n
    • Wissenschaftler k\u00f6nnten Zellen in Mikroskopieaufnahmen verfolgen oder Umweltver\u00e4nderungen in Satellitenbildern \u00fcberwachen<\/li>\n
    • F\u00fcr AVs, einschlie\u00dflich fahrerloser Autos, k\u00f6nnte SAM 2 die Objekterkennung in komplexen Verkehrsszenarien verbessern<\/li>\n
    • Wildtiersch\u00fctzer k\u00f6nnten SAM 2 zur \u00dcberwachung von Tierpopulationen in gro\u00dfen Gebieten einsetzen<\/li>\n
    • In AR\/VR kann sie genauere Interaktionen mit virtuellen Objekten in Live-Videos erm\u00f6glichen.<\/li>\n<\/ul>\n

      Getreu Metas Engagement f\u00fcr offene Forschung wird SAM 2 als Open-Source-Software ver\u00f6ffentlicht.\u00a0<\/span><\/p>\n

      Dazu geh\u00f6rt nicht nur das Modell, sondern auch der Datensatz, mit dem es trainiert wurde.\u00a0<\/span><\/p>\n

      Die Forscher suchen bereits nach M\u00f6glichkeiten, l\u00e4ngere Videos zu verarbeiten, die Leistung bei feinen Details zu verbessern und die f\u00fcr die Ausf\u00fchrung des Modells erforderliche Rechenleistung zu verringern.<\/span><\/p>\n

      Mit der Weiterentwicklung der Bildsegmentierungstechnologie wird sich die Art und Weise, wie wir mit Videoinhalten interagieren und sie analysieren, mit Sicherheit ver\u00e4ndern. <\/span><\/p>\n

      SAM 2 macht komplexe Editieraufgaben leichter zug\u00e4nglich und erm\u00f6glicht neue Formen der visuellen Analyse, indem es die Grenzen der visuellen Manipulation erweitert.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

      Die Forschungsabteilung von Meta hat SAM 2 (Segment Anything Model 2) vorgestellt, ein KI-System, das einen gro\u00dfen Fortschritt in der Videoanalyse darstellt. Dieses neue Modell erweitert die F\u00e4higkeiten seines Vorg\u00e4ngers SAM bei der Bildsegmentierung und wagt sich in den komplexeren Bereich der Videoanalyse vor. Die Segmentierung von Videos - die F\u00e4higkeit, bestimmte Objekte in einer sich bewegenden Szene zu identifizieren und zu verfolgen - ist seit langem eine Herausforderung f\u00fcr die KI. W\u00e4hrend Menschen m\u00fchelos einem Auto im Stra\u00dfenverkehr oder einer Person, die durch eine Menschenmenge l\u00e4uft, folgen k\u00f6nnen, haben KI-Systeme in der Regel Schwierigkeiten. Dies ist ein massives Problem f\u00fcr fahrerlose Autos und andere autonome Fahrzeuge (AVs),<\/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\/de\/2024\/07\/metas-sam-2-model-enables-accurate-video-segmentation-in-seconds\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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\/de\/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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