The Segmentation Anything Model (SAM) requires labor-intensive data labeling. We present Unsupervised SAM (UnSAM) for promptable and automatic whole-image segmentation that does not require human annotations.

Precise image segmentation provides clinical study with instructive information.

Specifically, SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process.

We select a representative subset consisting of 22 datasets and propose it as the MESS benchmark.

The annotation tool is a free painting tool, which allows the raters to freely draw the instance mask. We ask the raters to try to draw within the bbox, but if the object is obviously exceeding the bbox, then they can draw outside the bbox. The size of the stroke is adjustable.

Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes.

Our work taps directly into the representation created by 3DGS and maps each Gaussian to either the foreground or background.