Segment Anything

Alexander Kirillov, Eric Mintun, Nikhila Ravi · and 9 others (Meta AI)

Published 5 April 2023 · IEEE/CVF International Conference on Computer Vision (ICCV) · Conference paper

Summary

This paper introduces the Segment Anything project: a promptable image segmentation task, the Segment Anything Model (SAM), and the SA-1B dataset. SAM combines an image encoder, a flexible prompt encoder (points, boxes, masks, text), and a fast mask decoder to produce valid segmentation masks from arbitrary prompts. Trained on over 1 billion masks across 11 million images, SAM shows strong zero-shot transfer to many segmentation tasks without additional training.

Key findings

Introduces a promptable segmentation task that lets a single model generate valid masks for diverse prompt types.
SAM demonstrates strong zero-shot generalization, often competitive with or superior to prior fully supervised task-specific models.
Releases SA-1B, the largest segmentation dataset to date with over 1 billion masks on 11 million images.

Subjects & keywords

Artificial Intelligence computer vision image segmentation foundation model sam zero-shot

Cite this paper

APA

Alexander Kirillov, Eric Mintun, & Nikhila Ravi [and 9 others (Meta AI)] (2023). Segment Anything. IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.48550/arXiv.2304.02643

BibTeX

@inproceedings{kirillov2023segment,
  author    = {Alexander Kirillov and Eric Mintun and Nikhila Ravi and {and 9 others (Meta AI)}},
  title     = {Segment Anything},
  booktitle = {IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2023},
  doi       = {10.48550/arXiv.2304.02643},
  url       = {https://arxiv.org/abs/2304.02643}
}

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