SAM 3: Building a unified model architecture for detection and tracking

2025-12-08 · Source: AI at Meta · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Meta has developed SAM 3, a unified model architecture designed for both object detection and tracking. This model utilizes two distinct transformers, one for detection and another for tracking, which are coupled to perform the final task. The detection component operates on an image or frame level, identifying all object instances for a given concept prompt, which can be short text or visual examples. The tracking component is responsible for following individual objects across video frames. A key challenge addressed was the need for detection to learn similar representations for objects of the same category, while tracking requires distinct representations for each individual instance, even within the same category. SAM 3 builds upon prior Meta research, incorporating the SAM 2 model for tracking, a detection transformer for its detection architecture, and Llama as an AI annotation tool in its data engine.

Key takeaway

For AI scientists and computer vision engineers developing advanced perception systems, SAM 3 offers a unified approach to detection and tracking. Your teams should consider integrating this architecture for applications requiring both precise object identification and persistent instance tracking across video, potentially streamlining development for multimodal LLMs or creative editing tools. Evaluate its performance on your specific datasets to confirm its utility.

Key insights

SAM 3 unifies object detection and tracking using distinct transformers for each task.

Principles

• Detection learns similar object representations.
• Tracking learns distinct instance representations.

Method

SAM 3 couples separate detection and tracking transformers, leveraging SAM 2 for tracking and a detection transformer for its base architecture, with Llama assisting data annotation.

In practice

• Integrate SAM 3 into multimodal LLMs.
• Apply SAM 3 for Instagram photo edits.

Topics

• SAM 3
• Object Detection
• Object Tracking
• Transformer Architecture
• Multimodal AI

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, Deep Learning Engineer

Related on AIssential

• SAM 3 - AI at Meta — SAM 3 offers state-of-the-art object segmentation and tracking across images and videos using diverse text and visual prompts.
• SAM 3.1: Faster and More Accessible Real-Time Video Detection and Tracking With Multiplexing and Global Reasoning - AI at Meta — SAM 3.1 enhances real-time video object tracking via multiplexing, doubling processing speed and reducing GPU demands.
• SAM 3 for Video: Concept-Aware Segmentation and Object Tracking — SAM3 unifies detection, segmentation, and tracking in video by maintaining streaming memory and tracking state across frames.
• SAM 3: Concept-Based Visual Understanding and Segmentation — SAM 3 unifies detection, segmentation, and tracking of open-vocabulary concepts using text or visual prompts.
• Grounding Qwen3-VL Detection with SAM2 — Combining Qwen3-VL's detection with SAM2's segmentation enables robust, natural language-driven object grounding.
• SAM-Deep-EIoU: Selective Mask Propagation for Multi-Object Tracking — Selective mask propagation intelligently combines lightweight trackers with expensive VOS models to improve multi-object tracking efficiency and accuracy.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI at Meta.