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    Área de la Ciencia:

    • Inteligencia Artificial
    • Visión por Computadora

    Sus antecedentes:

    • La generalización de dominios (DG) tiene como objetivo que los modelos funcionen en dominios no vistos aprendiendo representaciones invariantes al dominio.
    • Los modelos de fundaciones visuales (VFM) muestran promesa para DG a través de la personalización de indicaciones, pero a menudo carecen de un enfoque en el desacoplamiento de características invariantes al dominio.

    Objetivo del estudio:

    • Mejorar la generalización entre dominios aprovechando las indicaciones de lenguaje de los VFM para desacoplar las características invariantes al dominio.
    • Proponer un marco novedoso, PADG, para la generalización robusta de dominios.

    Principales métodos:

    • Utilizar un modelo de lenguaje grande (LLM) para desacoplar las indicaciones textuales en componentes invariantes al dominio y específicos del dominio.
    • Emplear un módulo de Alineación de Representación Explícita de Peor Caso (WERA) para mejorar la invarianza visual a través de turnos de dominio simulados y alineación de representaciones.

    Principales resultados:

    • PADG supera consistentemente los métodos de vanguardia en los puntos de referencia de DG convencionales (PACS, VLCS, OfficeHome, DomainNet, TerraInc).
    • Demuestra la eficacia en el aprendizaje de representaciones robustas e invariantes al dominio.

    Conclusiones:

    • El marco PADG propuesto aborda eficazmente las limitaciones en DG basadas en VFM.
    • Logra un rendimiento superior al desacoplar características invariantes al dominio a través de indicaciones de lenguaje guiadas y alineación de representaciones.