摘要
In dairy farming,ensuring the health of each cow and minimizing economic losses requires individual monitoring,achieved through cow Re-Identification(Re-ID).Computer vision-based Re-ID relies on visually dis-tinguishing features,such as the distinctive coat patterns of breeds like Holstein.However,annotating every cow in each farm is cost-prohibitive.Our objective is to develop Re-ID methods applicable to both labeled and unlabeled farms,accommodating new individuals and diverse environments.Un-supervised Domain Adaptation(UDA)techniques bridge this gap,transferring knowledge from labeled source domains to unlabeled target domains,but have only been mainly designed for pedestrian and vehicle Re-ID applications.Our work introduces Cumulative Unsupervised Multi-Domain Adaptation(CUMDA)to address challenges of lim-ited identity diversity and diverse farm appearances.CUMDA accumulates knowledge from all domains,enhanc-ing specialization in known domains and improving generalization to unseen domains.Our contributions include a CUMDA method adapting to multiple unlabeled target domains while preserving source domain performance,along with extensive cross-dataset experiments on three cattle Re-ID datasets.These experiments demonstrate significant enhancements in source preservation,target domain specialization,and generalization to unseen domains.
