Comparative Analysis of CNNs and Vision Transformers for Age Estimation
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12251Palabras clave:
Visión por computadora, CNNs, Transformadores de visión, VGG-16, ResNet-50, EfficientNet-B0, ViT, CaiT, SwinResumen
Los transformadores de visión han adquirido recientemente una importancia significativa en las tareas de visión por ordenador debido a sus mecanismos de autoatención. Anteriormente, las CNN dominaban el campo de la visión por ordenador al lograr resultados notables en diversas aplicaciones como la clasificación de imágenes o el reconocimiento de objetos, entre otras. Sin embargo, con la llegada de los Transformadores de Visión, ha surgido una intensa competencia entre ambos. Este artículo presenta un análisis comparativo del rendimiento de las CNNs y los Transformadores de Visión para la tarea de estimación de la edad en los conjuntos de datos FG-NET y UTKFace. Realizamos la estimación de la edad utilizando seis modelos, incluidos tres modelos de CNN (VGG-16, ResNet-50, EfficientNet-B0) y tres modelos de transformadores de visión (ViT, CaiT, Swin). Nuestros resultados experimentales muestran que el transformador Swin superó tanto a la CNN como a los demás transformadores de visión.
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Derechos de autor 2025 Waqar Tanveer, Laura Fernández-Robles, Eduardo Fidalgo, Víctor González-Castro, Enrique Alegre, Milad Mirjalili
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
