Center for Functional Nanomaterials Seminar

This talk presents advances in multimodal deep learning methods through four interconnected studies, using agriculture as a testbed for specialized domain adaptation. While deep learning has shown remarkable success in general applications, its deployment in specialized domains faces unique challenges including limited data availability, domain-specific requirements, and computational constraints. We demonstrate how these challenges can be systematically addressed through: (1) optimization of Neural Radiance Fields that achieves 50% reduction in computational requirements while maintaining reconstruction quality, (2) development of a domain-specific benchmark (AgEval) for evaluating vision-language models, showing performance improvements from 46.24% to 73.37% in few-shot scenarios, (3) introduction of an assisted few-shot learning approach that enhances model performance to 80.45% through strategic example selection, and (4) presentation of an integrated system combining multiple deep learning modalities into a unified interface. Agriculture serves as an ideal experimental domain due to its complex data types, seasonal constraints, and high cost of errors. However, our methodological advances in computational efficiency, few-shot learning, and multimodal integration extend to any specialized domain requiring careful adaptation of deep learning technologies.