@inproceedings{nawaz-etal-2025-agriclip,
    title = "{A}gri{CLIP}: Adapting {CLIP} for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment",
    author = "Nawaz, Umair  and
      Muhammad, Awais  and
      Gani, Hanan  and
      Naseer, Muzammal  and
      Khan, Fahad Shahbaz  and
      Khan, Salman  and
      Anwer, Rao",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.644/",
    pages = "9630--9639",
    abstract = "Capitalizing on a vast amount of image-text data, large-scale vision-language pre-training has demonstrated remarkable zero-shot capabilities and has been utilized in several applications. However, models trained on general everyday web-crawled data often exhibit sub-optimal performance for specialized domains, likely due to domain shift. Recent works have tackled this problem for some domains (e.g., healthcare) by constructing domain-specialized image-text data. However, constructing a dedicated large-scale image-text dataset for sustainable areas of agriculture and livestock is still open to research. Further, this domain desires fine-grained feature learning due to the subtle nature of the downstream tasks (e.g., nutrient deficiency detection and livestock breed classification). To address this, we present AgriCLIP, a vision-language foundational model dedicated to the domain of agriculture and livestock. First, we propose a large-scale dataset named ALive that leverages a customized prompt generation strategy to overcome the scarcity of expert annotations. Our ALive dataset covers crops, livestock, and fishery, with around 600,000 image-text pairs. Second, we propose a training pipeline that integrates both contrastive and self-supervised learning to learn both global semantic and local fine-grained domain-specialized features. Experiments on a diverse set of 20 downstream tasks demonstrate the effectiveness of the AgriCLIP framework, achieving an absolute gain of 9.07{\%} in terms of average zero-shot classification accuracy over the standard CLIP adaptation via domain-specialized ALive dataset. Our ALive dataset and code can be accessible on Github."
}