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PicoSAM3 model enables real-time segmentation on image sensors

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PicoSAM3, a new lightweight segmentation model designed for real-time execution on edge devices and even directly on image sensors. This model, with 1.3 million parameters, utilizes a dense CNN architecture and incorporates techniques like region of interest prompt encoding and knowledge distillation from larger models. PicoSAM3 achieves strong performance on benchmarks like COCO and LVIS, and its quantized version can perform inference in under 12 milliseconds on the Sony IMX500 sensor, meeting its operational constraints. AI

IMPACT Enables real-time, privacy-preserving visual processing directly on edge devices and sensors.

RANK_REASON The cluster contains an academic paper detailing a new model and its performance benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PicoSAM3 model enables real-time segmentation on image sensors

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The cluster contains an academic paper detailing a new model and its performance benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Pietro Bonazzi, Nicola Farronato, Stefan Zihlmann, Haotong Qin, Michele Magno ·

    PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation

    arXiv:2603.11917v2 Announce Type: replace Abstract: Real-time, on-device segmentation is critical for latency-sensitive and privacy-aware applications such as smart glasses and Internet-of-Things devices. We introduce PicoSAM3, a lightweight promptable visual segmentation model o…

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