Sparse Point-Guided Fusion of Supervised and Self-Supervised Learning Model for Seaweed Segmentation
The ocean plays a critical role in sustainable development, particularly in climate change mitigation. Among marine ecosystems, blue carbon ecosystems are recognized as important natural carbon sinks. In this context, this paper addresses precise seaweed classification for blue carbon quantification in Ocean Digital Twin initiatives. Conventional methods, including supervised learning (limited by data scarcity and domain gaps) and self-supervised learning (unable to assign class labels), struggle with underwater complexities and diverse seaweed species. To overcome this, we propose a novel two-stage seaweed segmentation technique. This technique first utilizes Supervised and Self-supervis...
| Authors | Tatsuya Suzuki, Kazuya Ijuin, Hideki Tomimori et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 June 2026 |
| Last updated at source | 29 June 2026 |
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