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Updated: Jan 14, 2026

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Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models.

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    Summary

    Domain adaptation and generalization are key for AI to work across different environments. This survey explores multimodal approaches, from traditional methods to leveraging foundation models like CLIP, for improved real-world AI performance.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Domain adaptation and generalization are critical for AI models to perform reliably across diverse environments with varying data distributions.
    • Challenges arise from domain gaps caused by factors like lighting, weather, and sensor variations, especially in multimodal settings.
    • Significant progress has been made, with applications in action recognition and semantic segmentation.

    Purpose of the Study:

    • To survey recent advances in multimodal domain adaptation and generalization.
    • To analyze the evolution from traditional methods to foundation model-based approaches.
    • To provide a comprehensive overview of multimodal adaptation and generalization techniques.

    Main Methods:

    • Reviewing traditional approaches to multimodal domain adaptation and generalization.
    • Examining the impact of large-scale pre-trained multimodal foundation models (e.g., CLIP).
    • Analyzing multimodal test-time adaptation and the adaptation of foundation models themselves.

    Main Results:

    • Multimodal domain adaptation and generalization techniques have evolved significantly.
    • Foundation models offer enhanced capabilities for downstream adaptation and generalization.
    • The survey covers key areas including multimodal domain adaptation, test-time adaptation, and domain generalization.

    Conclusions:

    • Foundation models represent a significant advancement in multimodal adaptation and generalization.
    • Future research directions include addressing open challenges in multimodal AI.
    • The field is rapidly evolving, with ongoing research in diverse applications.