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El potencial del marco de modelado de redes neuronales de inspiración cognitiva para la visión por computadora
Guorun Li1, Lei Liu1, Xiaoyu Li1
1College of Engineering, China Agricultural University, Beijing, 100083, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|August 20, 2025
Resumen
Este estudio introduce un marco de modelado cognitivo (CMF) para mejorar las redes neuronales profundas de la visión (VDNNs) mediante la integración de la cognición visual humana. El nuevo modelo logra resultados de vanguardia, uniendo la ciencia cognitiva y la inteligencia artificial.
Área de la Ciencia:
- Inteligencia artificial
- Ciencias cognitivas
- Visión por computadora
Sus antecedentes:
- Las redes neuronales visuales profundas (VDNNs) actuales simulan principalmente mecanismos de atención, no el alcance completo de la cognición visual humana.
- Existe una brecha significativa entre la inteligencia artificial (IA) y la ciencia cognitiva (CS) en el modelado de la percepción visual.
Objetivo del estudio:
- Desarrollar un nuevo marco de modelado cognitivo (CMF) para las VNN que incorpore aspectos más amplios de la cognición visual humana.
- Integrar conceptos de memoria a largo plazo y algoritmos avanzados en las RNV para mejorar el rendimiento.
Principales métodos:
- Propuso un marco de modelado cognitivo de tres etapas (CMF): abstracción funcional, estructuración de operadores y agente de programación.
- Información previa definida de las características básicas de la imagen como contenido de la memoria a largo plazo.
- Introdujo el algoritmo de mapeo imparcial (UMA) utilizando la transformación rápida de Fourier (FFT) para el modelado de memoria en VNNs.
- Se han desarrollado unidades neuronales cognitivas visuales (VCNU) y un modelo de referencia (VCogM).
Principales resultados:
- Los modelos VCogM y VCNU desarrollados lograron un rendimiento de última generación (SOTA) en varias tareas de reconocimiento, incluida la clasificación de imágenes de paisajes naturales y agrícolas.
- El proceso de aprendizaje del modelo demostró la independencia de la distribución y la escala de los datos.
- Los resultados experimentales validaron la efectividad de los principios de modelado inspirados en la cognición.
Conclusiones:
- El marco de modelado cognitivo (CMF) y las VCNU asociadas ofrecen un enfoque más integral de la cognición visual en IA.
- La investigación proporciona una base para una integración más profunda entre la ciencia cognitiva y la inteligencia artificial.
- Los hallazgos destacan el potencial de la IA inspirada en el conocimiento para sistemas de reconocimiento visual robustos y generalizables.
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