COMPUTER VISION

SAM 2: Segment Anything in Images and Videos

July 29, 2024

Abstract

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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AUTHORS

Written by

Nikhila Ravi

Valentin Gabeur

Yuan-Ting Hu

Ronghang Hu

Chay Ryali

Tengyu Ma

Haitham Khedr

Roman Rädle

Chloe Rolland

Laura Gustafson

Eric Mintun

Junting Pan

Kalyan Vasudev Alwala

Nicolas Carion

Chao-Yuan Wu

Ross Girshick

Piotr Dollar

Christoph Feichtenhofer

Publisher

ArXiv

Research Topics

Computer Vision

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