  In this paper, we introduce Semantic-SAM, a universal image segmentation
model to enable segment and recognize anything at any desired granularity. Our
model offers two key advantages: semantic-awareness and granularity-abundance.
To achieve semantic-awareness, we consolidate multiple datasets across three
granularities and introduce decoupled classification for objects and parts.
This allows our model to capture rich semantic information. For the
multi-granularity capability, we propose a multi-choice learning scheme during
training, enabling each click to generate masks at multiple levels that
correspond to multiple ground-truth masks. Notably, this work represents the
first attempt to jointly train a model on SA-1B, generic, and part segmentation
datasets. Experimental results and visualizations demonstrate that our model
successfully achieves semantic-awareness and granularity-abundance.
Furthermore, combining SA-1B training with other segmentation tasks, such as
panoptic and part segmentation, leads to performance improvements. We will
provide code and a demo for further exploration and evaluation.
