Semantic-SAM: Segment and Recognize Anything at Any Granularity
Feng Li Hao Zhang Peize Sun Xueyan Zou Shilong Liu Jianwei Yang Chunyuan Li Lei Zhang Jianfeng Gao
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.