Guide dog training programs collect extensive behavioral and performance data, but trainers often struggle to utilize this information because it is locked in complex databases requiring technical expertise. To bridge this gap, this study introduces a multi-agent conversational chatbot that helps trainers easily access and interpret data about guide dogs. Using natural language queries, handlers can connect to a machine learning pipeline to compare historical records and predict training outcomes. An evaluation with professional trainers demonstrated that this dialogue-based interface made the underlying behavioral information highly approachable and interpretable. Ultimately, the system transforms scattered technical metrics into actionable insights, successfully empowering trainers to make practical, data-driven decisions.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection
A major challenge in training accurate facial landmark detection models
