构建农业元宇宙内容检索系统，用自然语言精准查找虚拟展馆
Hierarchical Vision-Language Retrieval of Educational Metaverse Content in Agriculture
- 设计分层视觉-语言模型，实现图文跨模态匹配
- 在457个农业展馆数据上达62%召回率，优于现有基准6%-11%
- 适合教育科技、智能农业与元宇宙应用开发者参考
每天都有大量农业与园艺类教育内容上传至网络。将这些视频或资料有意义地组织起来，可显著提升学习效率。利用元宇宙技术，用户可在交互式沉浸环境中探索教育内容，但如何高效搜索符合兴趣的场景仍是挑战。现有数据集规模小，难以支撑先进模型训练。本文提出两个主要贡献：一是构建包含457个农业主题虚拟博物馆（AgriMuseums）的新数据集，每个均配有文本描述；二是设计分层视觉-语言模型，支持通过自然语言查询检索相关展览。实验表明，该方法在测试中达到约62% R@1和78% MRR，且在现有基准上性能提升最高达6% R@1和11% MRR。广泛评估验证了设计合理性。代码与数据集已开源。
原文摘要 · Abstract (English)
Every day, a large amount of educational content is uploaded online across different areas, including agriculture and gardening. When these videos or materials are grouped meaningfully, they can make learning easier and more effective. One promising way to organize and enrich such content is through the Metaverse, which allows users to explore educational experiences in an interactive and immersive environment. However, searching for relevant Metaverse scenarios and finding those matching users' interests remains a challenging task. A first step in this direction has been done recently, but existing datasets are small and not sufficient for training advanced models. In this work, we make two main contributions: first, we introduce a new dataset containing 457 agricultural-themed virtual museums (AgriMuseums), each enriched with textual descriptions; and second, we propose a hierarchical vision-language model to represent and retrieve relevant AgriMuseums using natural language queries. In our experimental setting, the proposed method achieves up to about 62\% R@1 and 78\% MRR, confirming its effectiveness, and it also leads to improvements on existing benchmarks by up to 6\% R@1 and 11\% MRR. Moreover, an extensive evaluation validates our design choices. Code and dataset are available at https://github.com/aliabdari/Agricultural_Metaverse_Retrieval .
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