SAM 3: Segment Anything with Concepts

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#### RESEARCH

#### COMPUTER VISION

SAM 3: Segment Anything with Concepts

November 19, 2025

Abstract

We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short noun phrases (e.g., “yellow school bus”), image exemplars, or a combination of both. Promptable Concept Segmentation (PCS) takes such prompts and returns segmentation masks and unique identities for all matching object instances. To advance PCS, we build a scalable data engine that produces a high-quality dataset with 4M unique concept labels, including hard negatives, across images and videos. Our model consists of an image-level detector and a memory-based video tracker that share a single backbone. Recognition and localization are decoupled with a presence head, which boosts detection accuracy. SAM 3 delivers a 2× gain over existing systems in both image and video PCS, and improves previous SAM capabilities on visual segmentation tasks. We open source SAM 3 along with our new Segment Anything with Concepts (SA-Co) benchmark for promptable concept segmentation.

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#### AUTHORS

Written by

Nicolas Carion

Laura Gustafson

Yuan-Ting Hu

Shoubhik Debnath

Ronghang Hu

Didac Suris Coll-Vinent

Chaitanya Ryali

Kalyan Vasudev Alwala

Haitham Khedr

Andrew Huang

Jie Lei

Tengyu Ma

Baishan Guo

Arpit Kalla

Markus Marks

Joseph Greer

Meng Wang

Peize Sun

Roman Rädle

Triantafyllos Afouras

Effrosyni Mavroudi

Katherine Xu

Tsung-Han Wu

Yu Zhou

Liliane Momeni

Rishi Hazra

Shuangrui Ding

Sagar Vaze

Francois Porcher

Feng Li

Siyuan Li

Aishwarya Kamath

Ho Kei Cheng

Piotr Dollar

Nikhila Ravi

Kate Saenko

Pengchuan Zhang

Christoph Feichtenhofer

Publisher

arxiv

Research Topics

Computer Vision

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