Segment objects at many granularities by generating masks at multiple levels with Semantic-SAM
Segment objects at many granularities by generating masks at multiple levels with Semantic-SAM
Semantic-SAM: Segment and Recognize Anything at Any Granularity
arXiv paper abstract https://arxiv.org/abs/2307.04767
arXiv PDF paper https://arxiv.org/pdf/2307.04767.pdf
… introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity.
… model offers two key advantages: semantic-awareness and granularity-abundance.
To achieve semantic-awareness … consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts.
This allows … model to capture rich semantic information.
For the multi-granularity capability … propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks.
… demonstrate that … model successfully achieves semantic-awareness and granularity-abundance …
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