Most existing underwater instance segmentation approaches are constrained by close-vocabulary prediction, limiting their ability to recognize novel marine categories. To support evaluation, we introduce \textbf{MARIS} (\underline{Mar}ine Open-Vocabulary \underline{I}nstance \underline{S}egmentation), the first large-scale fine-grained benchmark for underwater Open-Vocabulary (OV) segmentation, featuring a limited set of seen categories and diverse unseen categories. Although OV segmentation has shown promise on natural images, our analysis reveals that transfer to underwater scenes suffers from severe visual degradation (e.g., color attenuation) and semantic misalignment caused by lack underwater class definitions. To address these issues, we propose a unified framework with two complementary components. The Geometric Prior Enhancement Module (\textbf{GPEM}) leverages stable part-level and structural cues to maintain object consistency under degraded visual conditions. The Semantic Alignment Injection Mechanism (\textbf{SAIM}) enriches language embeddings with domain-specific priors, mitigating semantic ambiguity and improving recognition of unseen categories. Experiments show that our framework consistently outperforms existing OV baselines both In-Domain and Cross-Domain setting on MARIS, establishing a strong foundation for future underwater perception research.

翻译：现有水下实例分割方法大多受限于封闭词汇预测，难以识别新型海洋类别。为支持评估，我们提出首个面向水下开放词汇分割的大规模细粒度基准数据集 **MARIS**（**Mar**ine Open-Vocabulary **I**nstance **S**egmentation），该数据集包含有限已知类别与多样未知类别。尽管开放词汇分割在自然图像上已展现潜力，但我们的分析表明，将其迁移至水下场景时存在严重的视觉退化（如色彩衰减）以及因缺乏水下类别定义导致的语义失配问题。为解决这些挑战，我们提出一个包含两个互补组件的统一框架：几何先验增强模块（**GPEM**）利用稳定的部件级与结构线索，在视觉退化条件下保持目标一致性；语义对齐注入机制（**SAIM**）通过领域先验知识增强语言嵌入，缓解语义歧义并提升对未知类别的识别能力。实验表明，在MARIS数据集上，我们的框架在领域内与跨域设置中均持续优于现有开放词汇基线方法，为未来水下感知研究奠定了坚实基础。