AquaSAM：水下图像前景分割
AquaSAM: Underwater Image Foreground Segmentation
AI总结：
AquaSAM首次将SAM扩展到水下图像分割，通过在SUIM数据集上自动提取标签并进行简单微调，在八类水下任务中平均DSC提升7.13%、mIoU提升8.27%，尤其在珊瑚礁等困难任务上优于默认SAM。
摘要：
Segment Anything Model（SAM）已经彻底改变了自然图像分割，然而其在水下图像上的表现仍然受限。本工作提出了AquaSAM，这是首次尝试将SAM的成功扩展到水下图像，目的是创建一种用于分割各种水下目标的通用方法。为此，我们首先在SUIM数据集中自动分类并提取各种标签。随后，我们开发了一种简单的微调方法，使SAM适应一般的前景水下图像分割。通过在包括人类潜水员在内的八个分割任务上进行的大量实验，我们证明AquaSAM优于默认的SAM模型，尤其是在珊瑚礁等困难任务上。AquaSAM在水下分割任务中实现了平均Dice相似系数（DSC）7.13（%）的提升，以及平均mIoU 8.27（%）的提升。
英文摘要：
The Segment Anything Model (SAM) has revolutionized natural image segmentation, nevertheless, its performance on underwater images is still restricted. This work presents AquaSAM, the first attempt to extend the success of SAM on underwater images with the purpose of creating a versatile method for the segmentation of various underwater targets. To achieve this, we begin by classifying and extracting various labels automatically in SUIM dataset. Subsequently, we develop a straightforward fine-tuning method to adapt SAM to general foreground underwater image segmentation. Through extensive experiments involving eight segmentation tasks like human divers, we demonstrate that AquaSAM outperforms the default SAM model especially at hard tasks like coral reefs. AquaSAM achieves an average Dice Similarity Coefficient (DSC) of 7.13 (%) improvement and an average of 8.27 (%) on mIoU improvement in underwater segmentation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
