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.
AUTHORS
Written by
Ronghang Hu
Peize Sun
Triantafyllos Afouras
Effrosyni Mavroudi
Katherine Xu
Tsung-Han Wu
Yu Zhou
Liliane Momeni
Shuangrui Ding
Sagar Vaze
Francois Porcher
Feng Li
Siyuan Li
Aishwarya Kamath
Ho Kei Cheng
Andrew Huang
Arpit Kalla
Baishan Guo
Chaitanya Ryali
Didac Suris Coll-Vinent
Haitham Khedr
Jie Lei
Joseph Greer
Kalyan Vasudev Alwala
Kate Saenko
Laura Gustafson
Markus Marks
Meng Wang
Nicolas Carion
Pengchuan Zhang
Rishi Hazra
Roman Rädle
Shoubhik Debnath
Tengyu Ma
Yuan-Ting Hu
Publisher
arxiv
Research Topics
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