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English(EN) ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition

ReefNet 数据集发布,用于细粒度珊瑚礁识别

研究人员推出了 ReefNet,这是一个用于细粒度珊瑚礁识别的大规模数据集和基准。该数据集包含约 925,000 个珊瑚点标注,经过筛选并与世界海洋物种名录分类法对齐,创建了一个包含 39 个不同标签的基准。该研究评估了包括 DINOv2 和 GPT-5.4 在内的各种模型,证明了监督适应性显著提高了识别性能,DINOv2 在保留测试集上达到了 65.79% 的宏召回率。长尾识别和地理不平衡等挑战仍然是未来研究的关键领域。 AI

影响 为在专业领域对多模态模型进行细粒度视觉识别任务评估提供了一个新基准。

排序理由 发布特定研究领域的新数据集和基准。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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ReefNet 数据集发布,用于细粒度珊瑚礁识别

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发布特定研究领域的新数据集和基准。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Abdulwahab Felemban, Yahia Battach, Faizan Farooq Khan, Yuqian Fu, Xuhui Liu, Yesmeen M. Khattab, Yousef A. Radwan, Xiang Li, Natalie Dunn, Susanne B\"ahr, Tullia Isotta Terraneo, Fabio Marchese, Sara Beery, Luis Silva, Rhonda Suka, Karla Gonzalez Martin… ·

    ReefNet:用于细粒度珊瑚礁识别的大规模数据集和基准测试

    arXiv:2510.16822v4 Announce Type: replace Abstract: Coral reef monitoring requires scalable recognition of visually similar taxa across heterogeneous underwater surveys. We introduce ReefNet, a public dataset that aligns approximately 925K hard-coral point annotations from 76 Cor…