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Aprendizaje de Representaciones Esqueléticas Mediante Mezcla Contrastiva Aumentada por Ataques

Binqian Xu, Xiangbo Shu, Jiachao Zhang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 4, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio presenta un marco impulsado por ataques para el reconocimiento de acciones no supervisado, generando muestras positivas y negativas más difíciles para mejorar las representaciones esqueléticas y el aprendizaje contrastivo. El nuevo enfoque mejora la robustez y el rendimiento del modelo.

    Palabras clave:
    reconocimiento de accionesaprendizaje no supervisadoaprendizaje contrastivorepresentaciones esqueléticasaumento de datosvisión por computadoraaprendizaje automático

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    Área de la Ciencia:

    • Visión por Computadora
    • Aprendizaje Automático
    • Inteligencia Artificial

    Sus antecedentes:

    • El reconocimiento de acciones no supervisado se basa en el aprendizaje contrastivo con pares de muestras positivas y negativas.
    • Los métodos existentes utilizan aumentos de datos para la construcción de pares, pero estos a menudo carecen de variación semántica y difuminan los límites de los pares.
    • Las alteraciones aleatorias de la apariencia en los esqueletos no capturan diferencias semánticas significativas y debilitan los objetivos contrastivos.

    Objetivo del estudio:

    • Desarrollar un marco de aumento impulsado por ataques para generar perturbaciones semánticamente significativas en datos esqueléticos.
    • Crear muestras informativas positivas y negativas difíciles para el aprendizaje de representaciones robustas en el reconocimiento de acciones no supervisado.
    • Introducir un marco novedoso, Aprendizaje de Representaciones Esqueléticas Mediante Mezcla Contrastiva Aumentada por Ataques (A^2MC), para el aprendizaje contrastivo mejorado.

    Principales métodos:

    • Se propuso un módulo de Aumento por Ataque (Att-Aug) que integra perturbaciones dirigidas (basadas en ataques) y no dirigidas (basadas en aumentos) para generar muestras positivas difíciles.
    • Se introdujo un Mezclador Positivo-Negativo (PNM) para sintetizar negativos difíciles y desafiantes al mezclar características positivas y negativas difíciles.
    • Se implementó un banco de memoria mixto actualizado con negativos difíciles sintetizados para mejorar el aprendizaje contrastivo.

    Principales resultados:

    • El marco A^2MC genera eficazmente muestras positivas y negativas difíciles, ricas en semántica.
    • Los métodos propuestos mejoran significativamente la robustez de las representaciones esqueléticas para el reconocimiento de acciones.
    • Las evaluaciones en tres puntos de referencia muestran que el rendimiento de A^2MC es competitivo o superior a los métodos de vanguardia.

    Conclusiones:

    • El aumento impulsado por ataques proporciona una estrategia más efectiva para crear pares de muestras informativos en el aprendizaje contrastivo.
    • El marco A^2MC ofrece un enfoque novedoso y efectivo para el reconocimiento de acciones no supervisado basado en esqueletos.
    • El método demuestra el potencial de las perturbaciones semánticas explícitas para avanzar en el aprendizaje de representaciones.