Segment Anything Model 2
segment-anything-model-2-43d9a1a0·5 events·first seen 1mo agoAliases: Segment Anything Model 2, Segment Anything Model, Segment Anything Model 3
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Meta SAM 3 (Segment Anything Model 3) Released on GitHub
Meta / Facebook Research has released SAM 3, the third generation of their Segment Anything Model, with code for inference and finetuning, pretrained model checkpoints, and example notebooks. The repository has accumulated over 10,000 stars with strong daily momentum (+93). SAM 3 continues Meta's open-weights tradition in computer vision foundation models. No accompanying paper or technical blog post is referenced in this item.
UPenn PRONTO Team Uses Meta's SAM 2 and DINO for Autonomous Military Medical Triage in DARPA Challenge
The University of Pennsylvania's PRONTO team is applying Meta's Segment Anything Model 2 (SAM 2) and DINO/Grounding DINO models to autonomous robotic triage in DARPA's three-year mass casualty incident challenge. The multi-robot system uses drones and ground robots to locate victims, then runs parallel injury classification pipelines combining SAM, DINO, and pose estimation to assess heart rate, respiration, wounds, and amputations without requiring labeled training data. Results are surfaced to first responders via a mobile interface for real-time prioritization. Phase 2 concluded in October 2025, with Phase 3 expected to push toward deployment-ready performance.
USRA Applies SAM 2 Fine-Tuning for Real-Time Flood and River Monitoring
The Universities Space Research Association (USRA) and Meta are collaborating with the U.S. Geological Survey (USGS) to apply a fine-tuned version of SAM 2 for automated water segmentation in drone and satellite imagery, targeting real-time flood detection and river extent mapping. The fine-tuned model replaces a labor-intensive manual digitization workflow that was a key bottleneck in rapid-response image analysis. The system integrates with PlanetScope satellite imagery and USGS 3D Hydrography data, with case studies in the Chesapeake Bay area showing promise for nationwide deployment. The collaboration also anticipates leveraging the recently released SAM 3 for unified detection, segmentation, and tracking.
ActiveSAM: Training-free open-vocabulary segmentation via image-conditional class pruning on SAM 3
ActiveSAM is a training-free, zero-shot inference framework that wraps Segment Anything Model 3 (SAM 3) to perform open-vocabulary semantic segmentation more efficiently. It estimates an image-conditioned active class subset at low resolution before running full-resolution decoding only on retained classes, using bucketed prompt multiplexing and margin-aware background calibration. Across eight benchmarks, it outperforms the prior state-of-the-art SegEarth-OV3 by ~1.4 mIoU on average while running up to 5.5x faster on large-vocabulary datasets, with strong robustness to image corruption relevant to autonomous driving and embodied AI.
Meta Introduces SAM Audio: Unified Multimodal Model for Audio Separation with PE-AV, Benchmark, and Judge Model
Meta has released SAM Audio, a unified multimodal audio separation model that accepts text, visual, and temporal span prompts to isolate sounds from complex audio mixtures. The system is powered by Perception Encoder Audiovisual (PE-AV), an extension of Meta's open-source Perception Encoder released earlier in 2025, and uses a flow-matching diffusion transformer architecture. Alongside the model, Meta is releasing SAM Audio-Bench (the first in-the-wild audio separation benchmark) and SAM Audio Judge (an automatic evaluation model for audio separation). All components are available today via the Segment Anything Playground.