SAM / Grounding DINO / ImageBind (perception foundation models)

Editorial record

Plain-language summary

Segment Anything trains a promptable segmentation model on a billion-mask dataset so that a point, box, or text-linked prompt yields object masks for categories never explicitly labeled; Grounding DINO adds open-vocabulary detection from text queries, and ImageBind aligns six modalities into one embedding space. Represented by SAM, this family removed the need to collect labels and train a fresh model for each new segmentation or detection target, enabling zero-shot and interactive perception that downstream systems can prompt directly.