Hierarchical Vision-Language Retrieval of Educational Metaverse Content in Agriculture
This work addresses the challenge of organizing and retrieving educational Metaverse content for users in agriculture, but it is incremental as it builds on existing vision-language retrieval methods with a new dataset.
The paper tackles the problem of searching for relevant educational Metaverse content in agriculture by introducing a new dataset of 457 agricultural-themed virtual museums and proposing a hierarchical vision-language model for retrieval, achieving up to 62% R@1 and 78% MRR with improvements on existing benchmarks.
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 .