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Related Experiment Video

Updated: Sep 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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RealignDiff: Boosting Text-to-Image Diffusion Model With Coarse-to-Fine Semantic Realignment.

Zutao Jiang, Guian Fang, Jianhua Han

    IEEE Transactions on Neural Networks and Learning Systems
    |July 18, 2025
    PubMed
    Summary
    This summary is machine-generated.

    RealignDiff improves text-to-image generation by refining image-text alignment. This two-stage method enhances visual quality and semantic accuracy, outperforming existing techniques.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision

    Background:

    • Text-to-image diffusion models generate realistic images but struggle with precise text-image alignment.
    • Challenges remain in ensuring generated visuals accurately reflect textual prompt concepts.

    Purpose of the Study:

    • To introduce RealignDiff, a novel two-stage method for enhancing text-image alignment in diffusion models.
    • To improve the semantic accuracy and visual quality of images generated from text prompts.

    Main Methods:

    • A coarse semantic realignment phase using a BLIP-2 based caption reward to measure text-image discrepancy.
    • A fine semantic realignment stage employing local dense captioning and reweighted attention modulation for refinement.

    Main Results:

    • RealignDiff significantly outperforms baseline realignment methods on MS-COCO and ViLG-300 datasets.
    • The method demonstrates substantial improvements in both visual quality and semantic similarity to the input prompt.

    Conclusions:

    • The proposed two-stage coarse-to-fine semantic realignment effectively addresses text-image alignment issues in diffusion models.
    • RealignDiff offers a promising approach for more accurate and semantically rich text-to-image synthesis.