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.
AUTHORS
Written by
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
Publisher
ArXiv
Research Topics
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