稀有节肢动物的分类学推理：结合密集图像描述与 RAG 实现可解释分类
Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification
AI总结：
本文提出融合密集图像描述、RAG 与 LLM 的可解释节肢动物分类流程，利用外部分类学文本提升稀有类群识别准确性并降低过度自信。
摘要：
在气候变化挑战紧迫、节肢动物生物多样性显著丧失的背景下，基于生物图像进行自动化分类学分类是一个受到密集研究的课题。然而，基于 CNN 或 ViT 等深度神经视觉架构的传统 AI 流程面临诸多局限，例如在类别的长尾部分性能下降，以及无法对其预测进行推理。我们将图像描述和检索增强生成（RAG）与大型语言模型（LLM）相结合，以增强生物多样性监测，并在表征稀有和未知节肢动物物种方面展现出特别的前景。尽管朴素的视觉-语言模型（VLM）擅长对常见物种的图像进行分类，但 RAG 模型通过将分类学特征的显式文本描述与来自外部来源的上下文生物多样性文本数据相匹配，实现了对更稀有分类单元的分类。与朴素 LLM 相比，RAG 模型在降低过度自信和提高准确性方面展现出前景，表明其能够捕捉分类层级的细微差别，尤其是在具有挑战性的科和属层级。我们的研究结果凸显了现代视觉-语言 AI 流程支持生物多样性保护举措的潜力，并强调全面数据整理以及与公民科学平台协作的作用，以改进物种识别、未知物种表征，并最终为保护策略提供依据。
英文摘要：
In the context of pressing climate change challenges and the significant biodiversity loss among arthropods, automated taxonomic classification from organismal images is a subject of intense research. However, traditional AI pipelines based on deep neural visual architectures such as CNNs or ViTs face limitations such as degraded performance on the long-tail of classes and the inability to reason about their predictions. We integrate image captioning and retrieval-augmented generation (RAG) with large language models (LLMs) to enhance biodiversity monitoring, showing particular promise for characterizing rare and unknown arthropod species. While a naive Vision-Language Model (VLM) excels in classifying images of common species, the RAG model enables classification of rarer taxa by matching explicit textual descriptions of taxonomic features to contextual biodiversity text data from external sources. The RAG model shows promise in reducing overconfidence and enhancing accuracy relative to naive LLMs, suggesting its viability in capturing the nuances of taxonomic hierarchy, particularly at the challenging family and genus levels. Our findings highlight the potential for modern vision-language AI pipelines to support biodiversity conservation initiatives, emphasizing the role of comprehensive data curation and collaboration with citizen science platforms to improve species identification, unknown species characterization and ultimately inform conservation strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
