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Separación de la modalidad unificada: un marco de lenguaje de visión para la adaptación del dominio no supervisado

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    Este estudio introduce un nuevo marco para la adaptación de dominio sin supervisión (UDA) utilizando modelos de lenguaje visual (VLM). Aborda efectivamente la brecha de modalidad, mejorando el rendimiento y la eficiencia computacional en tareas interdisciplinarias.

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    Área de la Ciencia:

    • Inteligencia artificial
    • Visión por computadora
    • Procesamiento del lenguaje natural

    Sus antecedentes:

    • La adaptación de dominio sin supervisión (UDA) tiene como objetivo generalizar los modelos de dominios de origen etiquetados a dominios de destino sin etiquetar.
    • Los modelos de lenguaje de visión (VLM) previamente entrenados son prometedores en UDA al alinear las incorporaciones de visión y texto, pero la brecha de modalidad dificulta el rendimiento.
    • La UDA directa con VLM a menudo transfiere solo conocimientos invariables en la modalidad, lo que lleva a resultados subóptimos.

    Objetivo del estudio:

    • Proponer un marco unificado para la AUD que aborde la brecha de modalidad en los MAV.
    • Desenredar y manejar por separado los componentes específicos de la modalidad y los invariantes de la modalidad para una mejor adaptación.
    • Introducir una métrica de discrepancia de modalidad para la categorización de muestras y la adaptación dirigida.

    Principales métodos:

    • Se propone un marco unificado de separación de modalidades para desentrañar las características de VLM en componentes invariantes específicos de cada modalidad.
    • Los pesos de conjunto adaptativos a la modalidad se determinan en el momento de la prueba para maximizar la sinergia de los componentes.
    • Una métrica de discrepancia de modalidad categoriza las muestras, lo que permite el uso específico de muestras invariantes de modalidad para la alineación y muestras inciertas para la anotación.

    Principales resultados:

    • El marco propuesto consigue un aumento del rendimiento de hasta un 9% en las tareas de la ADU.
    • El método demuestra un aumento de 9 veces en la eficiencia computacional en comparación con los enfoques existentes.
    • Los experimentos en diversos entornos validan la eficacia y la robustez del marco.

    Conclusiones:

    • El marco unificado de separación de modalidades cierra efectivamente la brecha de modalidades en los VLM para la AUD.
    • El enfoque mejora la generalización entre dominios aprovechando tanto el conocimiento específico de la modalidad como el invariante.
    • Este trabajo ofrece una solución computacionalmente eficiente y de alto rendimiento para UDA con VLM.