SAM 3: Segment Anything with Concepts
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.
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Nicolas Carion
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2025-11-25T16:18:21.295Z
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Nicolas Carion
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Laura Gustafson
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Laura Gustafson
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Yuan-Ting Hu
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Yuan-Ting Hu
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Shoubhik Debnath
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Shoubhik Debnath
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Ronghang Hu
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2025-11-25T16:18:46.407Z
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Ronghang Hu
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Didac Suris
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Didac Suris
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Chaitanya Ryali
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Haitham Khedr
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Haitham Khedr
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Andrew Huang
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Jie Lei
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Jie Lei
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Tengyu Ma
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Tengyu Ma
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Baishan Guo
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Baishan Guo
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Arpit Kalla
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Arpit kalla
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Markus Marks
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Markus Marks
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Joseph Greer
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Meng Wang
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Peize Sun
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Roman Rädle
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Roman Rädle
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Triantafyllos Afouras
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Triantafyllos Afouras
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Effrosyni Mavroudi
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Effrosyni Mavroudi
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Katherine Xu
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Tsung-Han Wu
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Yu Zhou
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Liliane Momeni
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Ho Kei Cheng
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Ho Kei Cheng
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Piotr Dollár
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Kate Saenko
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Pengchuan Zhang
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Christoph Feichtenhofer
SAM 3: Segment Anything with Concepts
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.
Segment Anything Model 3 achieves state-of-the-art performance in promptable concept segmentation and tracking by leveraging a unified model architecture with decoupled recognition and localization.
Segment Anything Model Promptable Concept Segmentation concept prompts segmentation masks unique identities scalable data engine image-level detector memory-based video tracker presence head visual segmentation tasks Segment Anything with Concepts benchmark