AI-SAM: Automatic and Interactive Segment Anything Model
Yimu Pan Sitao Zhang Alison D. Gernand Jeffery A. Goldstein James Z. Wang
Semantic segmentation is a core task in computer vision. Existing methods are
generally divided into two categories: automatic and interactive. Interactive
approaches, exemplified by the Segment Anything Model (SAM), have shown promise
as pre-trained models. However, current adaptation strategies for these models
tend to lean towards either automatic or interactive approaches. Interactive
methods depend on prompts user input to operate, while automatic ones bypass
the interactive promptability entirely. Addressing these limitations, we
introduce a novel paradigm and its first model: the Automatic and Interactive
Segment Anything Model (AI-SAM). In this paradigm, we conduct a comprehensive
analysis of prompt quality and introduce the pioneering Automatic and
Interactive Prompter (AI-Prompter) that automatically generates initial point
prompts while accepting additional user inputs. Our experimental results
demonstrate AI-SAM's effectiveness in the automatic setting, achieving
state-of-the-art performance. Significantly, it offers the flexibility to
incorporate additional user prompts, thereby further enhancing its performance.
The project page is available at https://github.com/ymp5078/AI-SAM.