Metadata Information
Metadata, descriptive information accompanying data, is increasingly recognized as a crucial element enhancing data analysis and application across diverse scientific domains. Current research focuses on integrating metadata into machine learning models, particularly using transformer networks and other deep learning architectures, to improve prediction accuracy, data retrieval, and anomaly detection in various applications, from time series forecasting to medical image analysis. This improved utilization of metadata promises to enhance the reliability, reproducibility, and efficiency of scientific workflows and data-driven applications, ultimately leading to more robust and insightful results.
Papers
VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments
Oliver Grau, Korbinian Hagn
Decoupled conditional contrastive learning with variable metadata for prostate lesion detection
Camille Ruppli, Pietro Gori, Roberto Ardon, Isabelle Bloch
Metadata Improves Segmentation Through Multitasking Elicitation
Iaroslav Plutenko, Mikhail Papkov, Kaupo Palo, Leopold Parts, Dmytro Fishman