多标签分类用于使用视觉基础模型与适配器学习的多时空,多空间珊瑚礁状况监测
Xinlei Shao1, Hongruixuan Chen2, Fan Zhao3
1Department of Socio-Cultural Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8563, Japan.
Marine pollution bulletin
|November 27, 2025
概括
本研究介绍了一种使用DINOv2和低等级适应 (LoRA) 来从水下图像中分类珊瑚礁状况的高效方法. 这种方法提高了准确性,同时大大降低了保护工作的计算成本.
科学领域:
- 海洋生物学 海洋生物学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 珊瑚礁提供重要的生态系统服务,但受到气候变化和人类活动的威胁.
- 深度学习模型与复杂的水下图像作斗争,以进行珊瑚礁状况分类.
- 视觉基础模型提供高精度,但需要大量资源进行微调.
研究的目的:
- 开发一种高效的方法来使用基础模型对珊瑚礁状况进行分类.
- 为了应对微调大型模型的计算和碳足迹挑战.
- 评估适应生态图像分析基础模型的性能和通用性.
主要方法:
- 整合DINOv2视觉基础模型与低级适应 (LoRA) 微调.
- 利用了来自泰国Koh Tao的15个潜水地点的多时代水下图像.
- 在公民科学基础上的保护计划中采用通用标签标准.
主要成果:
- DINOv2-LoRA模型实现了64.77%的优异匹配比率,超过了传统模型 (60.34%).
- LoRA显著降低了可训练参数,从1136.50M降至5.91M.
- 通过转移学习,在不同季节和空间环境中表现出异常的概括性.
结论:
- DINOv2-LoRA方法为珊瑚礁状况分类提供了一种高效和准确的方法.
- 这项研究是第一个在多时空和多空间环境中探索有效的基础模型适应多标签珊瑚礁分类的研究.
- 拟议的方法为珊瑚礁监测,保护和管理提供了有价值的工具.
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