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arXiv · 2307.04767

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
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Abstract

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.

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Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao. 2023-07-10. Semantic-SAM: Segment and Recognize Anything at Any Granularity. https://arxiv.org/abs/2307.04767

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