Quantitative Analysis
Quantitative analysis encompasses the development and application of mathematical and computational methods to extract meaningful insights from diverse data sources. Current research focuses on improving the accuracy and interpretability of these analyses, employing techniques like information theory, deep learning (including variations of UNet and Transformer architectures), and Bayesian nonparametric models, depending on the application. This field is crucial for advancing numerous scientific disciplines and practical applications, ranging from medical image analysis and environmental monitoring to improving the efficiency and explainability of AI systems in various domains. The development of novel metrics and benchmark datasets is also a key area of focus, enabling more robust comparisons and advancements in the field.
Papers
Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study
Filippos Gouidis, Theodore Patkos, Antonis Argyros, Dimitris Plexousakis
Quantitative analysis of visual representation of sign elements in COVID-19 context
María Jesús Cano-Martínez, Miguel Carrasco, Joaquín Sandoval, César González-Martín