Poster Sat, Apr 25, 2026 • 6:30 AM – 9:00 AM PDT Pavilion 4 P4-#3406

SAM 3: Segment Anything with Concepts

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

Project Page

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 doubles the accuracy of 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.