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Related Experiment Video

Updated: May 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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TRTST: Arbitrary High-Quality Text-Guided Style Transfer with Transformers.

Haibo Chen, Zhoujie Wang, Lei Zhao

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 3, 2025
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    Summary

    This study introduces TRTST, a novel transformer-based framework for text-guided style transfer. It achieves unpaired style transfer with improved visual quality and content preservation, overcoming limitations of previous methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Text-guided style transfer offers flexible image repainting using text prompts.
    • Existing methods struggle with visual quality, generalization, and paired data requirements.

    Purpose of the Study:

    • To develop a novel transformer-based framework for unpaired arbitrary text-guided style transfer.
    • To significantly improve the visual quality and content preservation in text-guided style transfer.

    Main Methods:

    • Proposed TRTST framework combining text and image transformer encoders.
    • Utilized a multimodal co-attention module for stylization.
    • Introduced adaptive parametric positional encoding (APPE) and text-guided identity loss.

    Main Results:

    • Achieved unpaired arbitrary text-guided style transfer.
    • Demonstrated significant improvements in visual quality.
    • Showcased superior content preservation compared to existing methods.

    Conclusions:

    • TRTST effectively addresses limitations of prior text-guided style transfer techniques.
    • The proposed framework offers a powerful solution for high-quality, flexible image stylization.