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InstructSee: Recuperación multimodal basada en instrucciones y retroalimentación con generación de consultas

Guihe Gu1,2,3, Yuan Xue1,2,3, Zhengqian Wu1,2,3

  • 1National Engineering Research Center for Multimedia Software (NERCMS), Wuhan 430072, China.

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|August 28, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un marco de conocimiento de instrucciones para la recuperación intermodal, mejorando los modelos de lenguaje grandes (LLM) para comprender mejor la intención del usuario a partir de instrucciones complejas para una mejor alineación del lenguaje visual.

Palabras clave:
Recuperación intermodalrefinamiento de la consulta dinámicaModelos de lenguaje grande (LLM)Aprendizaje de la representación multimodalrazonamiento semántico

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Área de la Ciencia:

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

Sus antecedentes:

  • La recuperación intermodal tiene como objetivo unir diferentes tipos de datos, en particular alinear imágenes con texto.
  • Los métodos actuales se enfrentan a desafíos en la interpretación de instrucciones complejas o en evolución del usuario para una recuperación precisa.
  • La comprensión de la intención implícita del usuario en las consultas multimodales sigue siendo una brecha de investigación significativa.

Objetivo del estudio:

  • Desarrollar un nuevo marco de aprendizaje de representación intermodal.
  • Mejorar la capacidad de los sistemas para capturar la intención del usuario a partir de instrucciones de lenguaje natural.
  • Mejorar la adaptabilidad y la precisión de los sistemas de recuperación multimodal.

Principales métodos:

  • Propuso un mecanismo de generación de consultas dinámicas conscientes de las instrucciones.
  • Capacidades de razonamiento semántico integrado de los grandes modelos de lenguaje (LLM).
  • Representaciones de consultas construidas dinámicamente y refinadas iterativamente basadas en instrucciones y comentarios del usuario.

Principales resultados:

  • El marco mejoró significativamente la precisión y la adaptabilidad de la recuperación en los parámetros de referencia estándar.
  • Superó a los métodos de referencia de consultas fijas existentes.
  • Capacidad demostrada de alineación y generalización intermodales.

Conclusiones:

  • El marco propuesto deduce y se adapta efectivamente a la intención implícita de recuperación.
  • La generación de consultas dinámicas aumentadas por LLM es crucial para la recuperación intermodal compleja basada en instrucciones.
  • El método ofrece una dirección prometedora para un acceso a la información multimodal más intuitivo y preciso.