title: SAM 2: Segment Anything in Images and Videos
summary: We present Segment Anything Model 2 (SAM 2), a foundation model towards
  solving promptable visual segmentation in images and videos. We build a data
  engine, which improves model and data via user interaction, to collect the
  largest video segmentation dataset to date. Our model is a simple transformer
  architecture with streaming memory for real-time video processing. SAM 2
  trained on our data provides strong performance across a wide range of tasks.
  In video segmentation, we observe better accuracy, using 3x fewer interactions
  than prior approaches. In image segmentation, our model is more accurate and 6x
  faster than the Segment Anything Model (SAM). We believe that our data, model,
  and insights will serve as a significant milestone for video segmentation and
  related perception tasks. We are releasing a version of our model, the dataset
  and an interactive demo.
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paper_authors.name: Kalyan Vasudev Alwala
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paper_authors.name: Chao-Yuan Wu
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paper_authors.fullname: Chao-Yuan Wu
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paper_authors.name: Ross Girshick
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paper_authors.fullname: Ross Girshick
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paper_authors.name: Piotr Dollár
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paper_authors.name: Christoph Feichtenhofer
paper_title: SAM 2: Segment Anything in Images and Videos
paper_summary: We present Segment Anything Model 2 (SAM 2), a foundation model towards
  solving promptable visual segmentation in images and videos. We build a data
  engine, which improves model and data via user interaction, to collect the
  largest video segmentation dataset to date. Our model is a simple transformer
  architecture with streaming memory for real-time video processing. SAM 2
  trained on our data provides strong performance across a wide range of tasks.
  In video segmentation, we observe better accuracy, using 3x fewer interactions
  than prior approaches. In image segmentation, our model is more accurate and 6x
  faster than the Segment Anything Model (SAM). We believe that our data, model,
  and insights will serve as a significant milestone for video segmentation and
  related perception tasks. We are releasing a version of our model, the dataset
  and an interactive demo.
paper_ai_summary: Segment Anything Model 2 (SAM 2) uses a transformer architecture with streaming memory to achieve high performance in image and video segmentation, requiring fewer interactions and faster processing than previous models.
paper_ai_keywords: transformer architecture streaming memory video segmentation image segmentation