SAM 3

Meta AI introduces SAM 3, a unified model that detects, segments, and tracks objects across images and videos using conceptual prompts like noun phrases or visual examples. This extends the Segment Anything capability to concept-based segmentation through Promptable Concept Segmentation (PCS). - **Scalable data engine with 4M concept labels:** The team built a data pipeline producing 4 million unique concept labels with hard negative examples across images and video content. This massive dataset enables training models to understand and segment objects based on abstract conceptual descriptions rather than just visual patterns. - **Unified architecture with presence head:** The model combines an image-level detector with memory-based video tracking, sharing a unified backbone. A novel presence head decouples recognition from localization, improving detection precision by separating the tasks of determining whether a concept exists from finding where it appears. - **2x improvement over existing approaches:** SAM 3 achieves double the performance of previous methods on concept segmentation tasks for both images and videos. It also improves upon earlier SAM iterations across standard visual segmentation benchmarks. - **Open release with SA-Co benchmark:** Meta releases the complete model weights and introduces SA-Co, a new benchmark specifically designed for evaluating promptable concept segmentation systems, providing standardized evaluation resources for future research.