@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://aclanthology.org/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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment
%A Nawaz, Umair
%A Muhammad, Awais
%A Gani, Hanan
%A Naseer, Muzammal
%A Khan, Fahad Shahbaz
%A Khan, Salman
%A Anwer, Rao
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F nawaz-etal-2025-agriclip
%X 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.
%U https://aclanthology.org/2025.coling-main.644/
%P 9630-9639
Markdown (Informal)
[AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment](https://aclanthology.org/2025.coling-main.644/) (Nawaz et al., COLING 2025)
ACL