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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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Visualizing Visual Adaptation
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Avances en Adaptación y Generalización Multimodal: De los Enfoques Tradicionales a los Modelos Fundacionales

Hao Dong, Moru Liu, Kaiyang Zhou

    IEEE transactions on pattern analysis and machine intelligence
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    La adaptación y generalización de dominios son clave para que la IA funcione en diferentes entornos. Esta encuesta explora enfoques multimodales, desde métodos tradicionales hasta el aprovechamiento de modelos fundacionales como CLIP, para mejorar el rendimiento de la IA en el mundo real.

    Palabras clave:
    adaptación multimodalgeneralización multimodalmodelos fundacionalesCLIPvisión por computadoraaprendizaje automáticointeligencia artificial

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    Área de la Ciencia:

    • Inteligencia Artificial
    • Visión por Computadora
    • Aprendizaje Automático

    Sus antecedentes:

    • La adaptación y generalización de dominios son fundamentales para que los modelos de IA funcionen de manera confiable en diversos entornos con distribuciones de datos variables.
    • Surgen desafíos debido a las brechas de dominio causadas por factores como la iluminación, el clima y las variaciones de los sensores, especialmente en entornos multimodales.
    • Se han logrado avances significativos, con aplicaciones en el reconocimiento de acciones y la segmentación semántica.

    Objetivo del estudio:

    • Examinar los avances recientes en la adaptación y generalización de dominios multimodales.
    • Analizar la evolución de los métodos tradicionales a los enfoques basados en modelos fundacionales.
    • Proporcionar una visión general completa de las técnicas de adaptación y generalización multimodales.

    Principales métodos:

    • Revisión de enfoques tradicionales para la adaptación y generalización de dominios multimodales.
    • Examen del impacto de los modelos fundacionales multimodales preentrenados a gran escala (por ejemplo, CLIP).
    • Análisis de la adaptación multimodal en tiempo de prueba y la adaptación de los propios modelos fundacionales.

    Principales resultados:

    • Las técnicas de adaptación y generalización de dominios multimodales han evolucionado significativamente.
    • Los modelos fundacionales ofrecen capacidades mejoradas para la adaptación y generalización posteriores.
    • La encuesta cubre áreas clave que incluyen la adaptación de dominios multimodales, la adaptación en tiempo de prueba y la generalización de dominios.

    Conclusiones:

    • Los modelos fundacionales representan un avance significativo en la adaptación y generalización multimodales.
    • Las direcciones futuras de investigación incluyen abordar los desafíos abiertos en IA multimodal.
    • El campo está en rápida evolución, con investigaciones en curso en diversas aplicaciones.