Underwater instance segmentation is a fundamental and critical step in various underwater vision tasks. However, the decline in image quality caused by complex underwater environments presents significant challenges to existing segmentation models. While the state-of-the-art USIS-SAM model has demonstrated impressive performance, it struggles to effectively adapt to feature variations across different channels in addressing issues such as light attenuation, color distortion, and complex backgrounds. This limitation hampers its segmentation performance in challenging underwater scenarios. To address these issues, we propose the MarineVision Adapter (MV-Adapter). This module introduces an adaptive channel attention mechanism that enables the model to dynamically adjust the feature weights of each channel based on the characteristics of underwater images. By adaptively weighting features, the model can effectively handle challenges such as light attenuation, color shifts, and complex backgrounds. Experimental results show that integrating the MV-Adapter module into the USIS-SAM network architecture further improves the model's overall performance, especially in high-precision segmentation tasks. On the USIS10K dataset, the module achieves improvements in key metrics such as mAP, AP50, and AP75 compared to competitive baseline models.

翻译：水下实例分割是各类水下视觉任务中的基础且关键步骤。然而，复杂水下环境导致的图像质量下降，对现有分割模型构成了重大挑战。尽管当前最先进的USIS-SAM模型已展现出令人印象深刻的性能，但在处理光衰减、颜色失真和复杂背景等问题时，其难以有效适应不同通道间的特征变化。这一局限阻碍了其在具有挑战性的水下场景中的分割性能。为解决这些问题，我们提出了MarineVision适配器（MV-Adapter）。该模块引入了一种自适应通道注意力机制，使模型能够根据水下图像的特征动态调整每个通道的特征权重。通过自适应特征加权，模型能够有效应对光衰减、颜色偏移和复杂背景等挑战。实验结果表明，将MV-Adapter模块集成到USIS-SAM网络架构中，可进一步提升模型的整体性能，尤其是在高精度分割任务中。在USIS10K数据集上，与具有竞争力的基线模型相比，该模块在mAP、AP50和AP75等关键指标上均取得了提升。