面向中文场景文本编辑的不确定性感知生成网络

Uncertainty-awared generative network for Chinese scene text editing

  • 摘要: 通过研究,提出了一种基于不确定性建模的中文场景文本编辑(Chinese scene text editing,CSTE)方法,并发现了1种有效的技术解决方案.该方法通过不确定性引导的调整机制优化预测噪声,提升噪声估计准确性,从而增强生成文本的清晰度和结构完整性.同时,通过过滤文本和图像特征中的无关信息,提高了跨模态对齐能力,实现了文本与背景纹理的融合.

     

    Abstract: A Chinese scene text editing (CSTE) method, based on research but incorporating uncertainty modeling and identifying effective technical solution, is proposed in this work. This new method optimizes prediction noise through an uncertainty-guided adjustment mechanism, improving the accuracy of noise estimation, thereby enhancing the clarity and structural integrity of the generated text. Additionally, by filtering irrelevant information from both textual and visual features, the method improves cross-modal alignment capabilities, achieving a seamless fusion of text and background textures.

     

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