香港科技大学团队开发出一款名为 Semantic-SAM 的图像分割 AI 模型，相比 Meta 的 SAM 模型，Semantic-SAM 具有更强的粒度和语义功能。该模型能够在不同粒度级别上分割和识别物体，并为分割出的实体提供语义标签。
研究团队通过联合训练 SA-1B 数据集、通用分割数据集和部件分割数据集，实现了多粒度分割任务和交互分割任务的优化。Semantic-SAM 基于 Mask DINO 框架进行开发，其模型结构主要改进在 decoder 部分，同时支持通用分割和交互式分割。
模型的训练过程中，采用解耦的物体分类和部件分类方法，以学习物体和部件的语义信息。实验结果表明，Semantic-SAM 在分割质量和粒度可控性方面优于 SAM 模型。总之，Semantic-SAM 是一款全面且强大的图像分割 AI 模型。
来源：站长之家
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