"The release of the Segment Anything Model 3 (SAM 3) marks a definitive transition in computer vision, shifting the focus from purely geometric object localization to a sophisticated, concept-driven understanding of visual scenes. Developed by Meta AI, SAM 3 is described as the first unified foundation model capable of detecting, segmenting, and tracking all instances of an open-vocabulary concept across images and videos via natural language prompts or visual exemplars."
"While its predecessors (i.e., SAM 1 and SAM 2) established the paradigm of Promptable Visual Segmentation (PVS) by allowing users to define objects via points, boxes, or masks, they remained semantically agnostic. As a result, they essentially functioned as high-precision geometric tools. SAM 3 transcends this limitation by introducing Promptable Concept Segmentation (PCS). This task internalizes semantic recognition and enables the model to "understand" user-provided noun phrases (NPs)."
Segment Anything Model 3 (SAM 3) shifts segmentation from geometric promptable methods to open-vocabulary concept segmentation that understands noun phrases. SAM 3 unifies detection, segmentation, and tracking of concept instances across images and videos via natural language prompts or visual exemplars. The architecture combines a Perception Encoder, a DETR-style detector, a Presence Head, and a streaming tracker to support concept grounding and temporal continuity. The SA-Co data engine provides large-scale concept supervision enabling learning of open-vocabulary concepts at scale. SAM 3 builds on SAM-1 and SAM-2 by adding semantic understanding to the promptable visual segmentation paradigm. Development environment setup and single-prompt examples demonstrate the basic image segmentation workflow using text prompts.
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