用视觉模型自动识别地甲虫，准确率达97%以上
BeetleVerse: A Study on Taxonomic Classification of Ground Beetles
- 用Vision and Language Transformer结合MLP头进行分类
- 在物种和属级别分别达到94%和97%准确率
- 适合生态监测与小样本、跨域场景应用
地甲虫是高度敏感且种类繁多的生物指示物种，对生物多样性监测至关重要。但因其形态差异细微，需专家手工鉴别，限制了广泛应用。本文在四个涵盖230多个属、1769个物种的长尾数据集上评估12种视觉模型，图像来源包括实验室控制环境与野外真实拍摄。研究还探讨了样本效率与域适应两个实际应用场景。结果表明，视觉与语言变压器结合MLP头表现最佳，在属级别准确率达97%，物种级别达94%。样本效率分析显示，训练数据可减少50%而性能损失极小。域适应实验揭示从实验室到野外图像存在显著域差距。本研究为大规模自动化甲虫分类奠定基础，并推动小样本学习与跨域适应在多样化长尾生态数据集中的应用。
原文摘要 · Abstract (English)
Ground beetles are a highly sensitive and speciose biological indicator, making them vital for monitoring biodiversity. However, they are currently an underutilized resource due to the manual effort required by taxonomic experts to perform challenging species differentiations based on subtle morphological differences, precluding widespread applications. In this paper, we evaluate 12 vision models on taxonomic classification across four diverse, long-tailed datasets spanning over 230 genera and 1769 species, with images ranging from controlled laboratory settings to challenging field-collected (in-situ) photographs. We further explore taxonomic classification in two important real-world contexts: sample efficiency and domain adaptation. Our results show that the Vision and Language Transformer combined with an MLP head is the best performing model, with 97% accuracy at genus and 94% at species level. Sample efficiency analysis shows that we can reduce train data requirements by up to 50% with minimal compromise in performance. The domain adaptation experiments reveal significant challenges when transferring models from lab to in-situ images, highlighting a critical domain gap. Overall, our study lays a foundation for large-scale automated taxonomic classification of beetles, and beyond that, advances sample-efficient learning and cross-domain adaptation for diverse long-tailed ecological datasets.
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