2 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.CV2025★ 2 cited
fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model
Yufeng Xie, Hanzhi Wu, Hongxiang Tong +4
Delineating farmland boundaries is essential for agricultural management such as crop monitoring and agricultural census. Traditional methods using remote sensing imagery have been…
q-bio.QM2024★ 1 cited
Identifying Cocoa Pollinators: A Deep Learning Dataset
Wenxiu Xu, Saba Ghorbani Bazegar, Dong Sheng +3
Cocoa is a multi-billion-dollar industry but research on improving yields through pollination remains limited. New embedded hardware and AI-based data analysis is advancing informa…
cs.CV2018
Testing the Efficient Network TRaining (ENTR) Hypothesis: initially reducing training image size makes Convolutional Neural Network training for image recognition tasks more efficient
Thomas Cherico Wanger, Peter Frohn
Convolutional Neural Networks (CNN) for image recognition tasks are seeing rapid advances in the available architectures and how networks are trained based on large computational i…