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English(EN) FSVL: Taxonomic Fast-Slow Vision-to-Language Collaborative Framework for Interpretable Confusable Fish Species Identification and Knowledge Generation in the Middle Yangtze River

新的FSVL框架通过双路径AI增强鱼类识别能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了FSVL，一个结合了快速和慢速路径的新型框架，用于识别长江中游流域的鱼类物种。快速路径使用ResNet50，实现了初步的高准确率，而使用YOLOv8n的慢速路径通过分析关键形态区域来精炼易混淆物种的识别。该系统与Qwen3-14B语言模型集成以生成知识，显著提高了科学名称和属的准确性，以及ROUGE-L分数，有助于生物多样性监测和渔业管理。 AI

影响 该框架有望通过更准确、更高效的物种识别来改善生物多样性监测和渔业管理。

排序理由 该条目描述了一个新的学术框架及其在特定任务上的性能，发布在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的FSVL框架通过双路径AI增强鱼类识别能力

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该条目描述了一个新的学术框架及其在特定任务上的性能，发布在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Meng Liang, Fucheng Wang, Lang Zhao, Jun Chen, Ziying Li, Yuxiang Zhao, Haozhuang Chi, Wenkai Fang, Qiong Zhou, Junlin Yang, Jinming Wu ·

    FSVL：用于长江中游可解释的混淆鱼类物种识别和知识生成的分类快速-慢速视觉到语言协同框架

    arXiv:2610.31880v1 Announce Type: cross Abstract: The Yangtze River Basin is a critical region for freshwater biodiversity conservation. The implementation of the Yangtze River Ten-Year Fishing Ban has increased the importance of effective aquatic biodiversity monitoring and spec…

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