Abstract
Robust behaviour recognition in real-world farm environments remains challenging due to several data-related limitations, including the scarcity of well-annotated livestock video datasets and the substantial domain gap between large-scale pre-training corpora and agricultural surveillance footage. To address these challenges, we propose Cattle-CLIP, a domain-adaptive vision-language framework that reformulates cattle behaviour recognition as cross-modal semantic alignment rather than purely visual classification. Instead of directly fine-tuning visual backbones, Cattle-CLIP incorporates a temporal integration module to extend image-level contrastive pre-training to video-based behaviour understanding, enabling consistent semantic alignment across time. To mitigate the distribution shift between web-scale image-text data used for the pre-trained model and real-world cattle surveillance footage, we further introduce tailored augmentation strategies and specialised behaviour prompts. Furt