AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment

• URL: http://arxiv.org/abs/2410.01407v1
• Date: Wed, 2 Oct 2024 10:33:49 GMT
• Title: AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment
• Authors: Umair Nawaz, Muhammad Awais, Hanan Gani, Muzammal Naseer, Fahad Khan, Salman Khan, Rao Muhammad Anwer,
• Abstract summary: AgriCLIP is a vision-language foundational model dedicated to the domain of agriculture and livestock. Our ALive dataset covers crops, livestock, and fishery, with around 600,000 image-text pairs. AgriCLIP framework achieves an absolute gain of 7.8% in terms of average zero-shot classification accuracy.
• Score: 35.35466045639057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Capitalizing on 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 area 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, 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 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 diverse set of 20 downstream tasks demonstrate the effectiveness of AgriCLIP framework, achieving an absolute gain of 7.8\% 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 at \href{https://github.com/umair1221/AgriCLIP/tree/main}{Github}.

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