针对水下少标签语义分割的类内差距的问题，提出一种具有信息交互的FUSS模型，通过将提取的询问图像全局特征信息和支持图像目标局部特征信息进行交互，实现对共性对象信息的有效关注。实验结果表明，在仅使用50张支持数据集情况下，与CaNet、PGNet和PaNet三种少标签语义分割算法对比，FUSS模型无论是在水下环境还是在地面图像上均优于这些算法。

Aiming at the problem  of intra class  gap  in underwater semantic segmentation wi th few lab els, a feed shot un- der water santic segmentation wi th few lab els, a feed shot un- der water seman tic se gmentation ( F USS) model wit h informa- tion i nteractio n was prop ose d. By int eractin g the ext racted global feature info rm ation of the qu ery im age with th e local feat ure infor mat ion of the su pportin g image obj ect, the effec- tive attention to t he com mon o bjec t inf ormation was re alized. T he experi me ntal resul ts sh ow th at the pro posed FUSS model is sup eri or to the three less label semantics egmentation algorithms of CaNet、 PGNet 和 PaNet either in un derwa ter environment or on the ground image in the case of using only 50 supporting data sets.