Segment Anything

Table of Contents

Title: Segment Anything

Authors: Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick

Published: Apr 5 2023

Link: https://arxiv.org/abs/2304.02643

Summary (Generated by Microsoft Copilot):

Introduction:

  • The Segment Anything (SA) project introduces a new task, model, and dataset for image segmentation, aiming to create a foundation model that can generalize to various tasks using prompt engineering.

Challenges:

  • Developing a model that supports flexible prompts and can output segmentation masks in real-time.
  • Collecting a large and diverse dataset for training.

Methods:

  • The model, Segment Anything Model (SAM), uses a powerful image encoder, a prompt encoder, and a lightweight mask decoder.
  • A data engine was developed to collect over 1 billion masks from 11 million images.

Novelties:

  • SAM can handle ambiguous prompts by predicting multiple valid masks.
  • The dataset, SA-1B, is the largest segmentation dataset to date.

Results:

  • SAM shows impressive zero-shot performance, often competitive with fully supervised models.

Performances:

  • Evaluated on 23 segmentation datasets, SAM produces high-quality masks and performs well on various downstream tasks.

Limitations:

  • Room for improvement remains, particularly in handling more complex segmentation tasks.

Discussion:

  • The project aims to foster research into foundation models for computer vision, with SAM and SA-1B available for research purposes.