摘要： 分割一切模型(SAM)在各种下游任务中得到了广泛的应用。海洋浮游动物物种形态复杂、透明度高、物种尺度大小不一，导致现有的分割模型难以适应从而分割精度较低。此外，缺乏像素级别实例标注的海洋浮游动物图像数据集阻碍了SAM在该领域分割任务中的探索研究。为了解决这些问题，构建一个具有像素级别精细化标注的实例分割数据集MZIS，其中包含25个物种类别与1908张浮游动物图像。针对海洋浮游动物场景进一步提出一种基于SAM的实例分割方法MZIS-SAM。具体来说：首先，为了弥补缺乏的海洋浮游动物语义类别信息，设计了一种浮游动物显微图像自适应的ViT（ZMA-ViT）编码器，提取浮游动物的视觉特征提示并融入网络；接着，设计了一个多尺度膨胀注意力聚合模块（MDAAM），用于整合编码器中的多层特征来增强多尺度特征表达；最后，设计了一个特征提示生成模块（FPGM）来自动生成视觉特征提示，实现端到端的实例分割掩码预测。实验结果表明，相比于现有的方法，MZIS-SAM在MZIS数据集上的 、 和 得分分别达到77.0%、97.7%与85.8%先进水平。

Abstract: The Segment Anything Model(SAM) has been widely applied in diverse downstream tasks.The complexityof species morphology, high transparency, and varying speciessizes of marine zooplankton pose significant challenges to the adaptability of existing segmentation models, often resulting in low segmentation accuracy.Moreover, the lack of datasets of marine zooplankton images has impeded the exploration of SAM for instance segmentation in this field. To address this issue, this paper constructs a Marine Zooplankton Instance Segmentation (MZIS) dataset with pixel-level fine-grained annotations, which contains 1908 zooplankton images of 25 species categories.Furthermore, this research proposes a Marine Zooplankton Instance Segmentation framework based on SAM, called MZIS-SAM for the Zooplankton images. Specifically, to compensate for the lack of semantic category information, MZIS-SAM first introducesaZooplankton Microimages Adaptive ViT(ZMA-ViT) encoder to extract visual feature prompts of zooplankton and incorporate them into the network.Subsequently, to enhance the multi-scale feature representation of zooplankton, a Multi-Scale Dilated Attention Aggregation Module(MDAAM) is designed that to progressively integrate multi-level features from SAM’s encoder.Finally, MZIS-SAM devises a Feature Prompt Generation Module(FPGM) to automatically generate visual feature prompts for end-to-end segmentation.The experimental results on the MZIS dataset show that compared to existing instance segmentation methods, MZIS-SAM achieves state-of-the-art performance with scores of 77.0%, 97.7%, and 85.8% on , , and , respectively.