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La retroalimentación contextual en el reconocimiento de objetos: un modelo computacional inspirado en la biología y
Elahe Soltandoost1, Karim Rajaei2, Reza Ebrahimpour3
1University of Padova, Department of Information Engineering, Via Gradenigo 6/b, Padova, 35131, Veneto, Italy; Shahid Rajaei University, Faculty of Engineering, Lavizan, Shahid Babaei Highway, Tehran, 16788-15811, Iran.
Vision research
|August 31, 2025
Resumen
Un nuevo modelo computacional basado en el contexto (CBM) mejora el reconocimiento de objetos con el contexto de la escena. Si bien es eficaz, la percepción visual humana se basa más en el procesamiento intrínseco de objetos, especialmente con oclusión.
Área de la Ciencia:
- Neurociencia computacional
- Visión por ordenador
- Psicología cognitiva
Sus antecedentes:
- El contexto de la escena influye significativamente en la percepción visual y el reconocimiento de objetos, especialmente en condiciones difíciles.
- Se sugieren mecanismos de arriba hacia abajo que involucran información de la escena que modula áreas cerebrales selectivas de objetos, pero no se entienden completamente.
Objetivo del estudio:
- Introducir un modelo computacional basado en el contexto (CBM) de inspiración biológica para el reconocimiento de objetos.
- Investigar el papel de los mecanismos de retroalimentación explícitos que integran el contexto de la escena en el reconocimiento de objetos.
- Comparar el rendimiento de CBM con un modelo estándar de retroalimentación y la percepción humana.
Principales métodos:
- Desarrolló un modelo computacional basado en el contexto (CBM) con dos vías: Object_CNN para las características del objeto y Place_CNN para el contexto de la escena.
- Comparó CBM con AlexNet (un modelo de avance estándar) en tareas de reconocimiento de objetos con degradación visual y oclusión.
- Realizó experimentos conductuales para comparar el reconocimiento de objetos humanos con el rendimiento de CBM y AlexNet.
Principales resultados:
- CBM superó significativamente a AlexNet en el reconocimiento de objetos, lo que demuestra el beneficio de la retroalimentación contextual para la entrada degradada.
- Los participantes humanos mostraron un beneficio modesto del contexto congruente, particularmente con altos niveles de oclusión.
- El reconocimiento humano se mantuvo sólido incluso sin contexto, lo que sugiere roles dominantes para el procesamiento de formas globales y procesos recurrentes locales.
Conclusiones:
- La retroalimentación contextual puede mejorar el rendimiento del modelo computacional para el reconocimiento de objetos.
- La percepción visual humana emplea estrategias distintas, que dependen en gran medida del procesamiento intrínseco de objetos y los mecanismos recurrentes locales.
- Los modelos futuros deben integrar la retroalimentación sensible al contexto con los procesos recurrentes locales para imitar mejor la resiliencia visual humana.
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