“Segment Anything”, Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick2023-04-05 (Transformer, AI scaling, active learning)⁠:

We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation.

Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed & privacy-respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks.

We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive—often competitive with or even superior to prior fully supervised results.

We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at segment-anything.com to foster research into foundation models for computer vision.