Roboflow

SAM 3: The Eyes for AI — Nikhila & Pengchuan (Meta Superintelligence), ft. Joseph Nelson (Roboflow)

Dec 18, 2025 · 1:15:04

Meta's SAM 3, introduced by Nikhila Ravi and Pengchuan Zhang alongside Roboflow CEO Joseph Nelson, unifies interactive segmentation, open-vocabulary detection, and video tracking into a single model that runs in 30ms on images and scales to real-time video on multi-GPU setups. The model uses concept prompts like "yellow school bus" to detect and segment every instance, separating recognition from localization via a presence token. Its data engine automated exhaustive annotation from two minutes per image down to 25 seconds using AI verifiers fine-tuned on Llama, while the new SACO benchmark contains over 200,000 unique concepts versus previous 1.2k. For video, decoupling the detector and tracker preserves object identity, and SAM 3 agents pair with multimodal LLMs like Gemini to handle complex visual reasoning. The real-world impact includes 106 million smart polygons created on Roboflow, saving an estimated 130+ years of labeling time across fields from cancer research to underwater trash cleanup.

Segment Anything 2: Memory + Vision = Object Permanence — with Nikhila Ravi and Joseph Nelson

Aug 7, 2024 · 1:00:44

Nikhila Ravi from FAIR and Joseph Nelson from Roboflow discuss Segment Anything 2 (SAM 2), which extends zero-shot object segmentation to video with memory and real-time interactive tracking. The model is one-third the size of SAM 1 (224M vs 630M parameters) and six times faster, using a novel memory attention mechanism with six-frame spatial memory and longer-term object pointers. Ravi explains the three-phase data engine that built the SA-V dataset of 51,000 videos, enabling SAM 2 to track arbitrary objects like a T-shirt or an octopus even when occluded. At Roboflow, users labeled 49 million images with SAM in its first year, saving an estimated 35 years of manual annotation time. The demo features swim lanes that show object visibility and allow refinement clicks to correct tracking mistakes, a key improvement over prior video segmentation models. SAM 2 also handles out-of-distribution domains like underwater footage, though screenshots remain challenging and may require fine-tuning.