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Updated: Jan 14, 2026

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CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey
IEEE transactions on pattern analysis and machine intelligence
|January 12, 2026
Resumen
Esta encuesta ofrece una visión general completa de las aplicaciones de preentrenamiento contrastivo de lenguaje e imagen (CLIP) en la generalización de dominio (DG) y la adaptación de dominio (DA). Categoriza métodos, analiza tendencias y discute desafíos para sistemas de IA robustos.
Área de la Ciencia:
- Inteligencia Artificial
- Aprendizaje Automático
- Visión por Computadora
Sus antecedentes:
- La generalización de dominio (DG) y la adaptación de dominio (DA) son vitales para la robustez de los modelos de IA en diversos entornos.
- CLIP ofrece sólidas capacidades de cero disparos para dominios no vistos.
- Se necesita una revisión sistemática del papel de CLIP en DG y DA.
Objetivo del estudio:
- Proporcionar una visión general unificada y en profundidad de DG y DA impulsados por CLIP.
- Establecer una taxonomía para escenarios de DG/DA (accesibilidad de la fuente, número, relaciones de etiquetas).
- Sintetizar estudios existentes e identificar lagunas de investigación.
Principales métodos:
- Categorización de métodos de DG: optimización de indicaciones y arquitecturas CLIP-as-backbone.
- Examen de enfoques de DA: fuente disponible y sin fuente, centrándose en la transferencia de conocimiento.
- Análisis de tendencias consolidados para DG y DA, identificando patrones y principios.
Principales resultados:
- CLIP mejora significativamente DG y DA al proporcionar características transferibles y capacidades de cero disparos.
- Una taxonomía estructurada ayuda a comprender diversos escenarios de DG/DA.
- Los análisis de tendencias revelan principios metodológicos y comportamientos dependientes del escenario.
Conclusiones:
- Esta encuesta sintetiza la investigación de DG/DA basada en CLIP, ofreciendo información para los profesionales.
- Identifica desafíos como la implementación, la integración de LLM, la interpretabilidad y el olvido catastrófico.
- Describe direcciones futuras para sistemas de DG/DA escalables y confiables basados en CLIP.
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