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Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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一个场景文本合成引擎通过从分解的现实世界数据中学习来实现.

Zhengmi Tang, Tomo Miyazaki, Shinichiro Omachi

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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    概括

    我们介绍DecompST,一个新的数据集和基于学习的文本合成 (LBTS) 引擎,以改善合成场景文本图像生成,用于训练深度学习模型. LBTS增强了文本集成,以获得更好的场景文本检测性能.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 场景文本图像合成对于训练深度神经网络至关重要,提供准确的注释数据.
    • 现有的方法通常依赖于无监督学习,因为缺乏合适的数据集,导致性能限制.
    • 之前的方法探索了基于规则的生成和基于2D和3D表面的基于学习的方法.

    研究的目的:

    • 促进基于学习的场景文本合成研究.
    • 引入一个新的数据集 (DecompST) 和一个基于学习的文本合成 (LBTS) 引擎.
    • 为了改善下游任务的现实合成场景文本图像的生成.

    主要方法:

    • DecompST数据集是从公共基准创建的,包括四边形BBoxes,冲击级别面具和文本删除图像.
    • 提出了一个基于学习的文本合成 (LBTS) 引擎,包括一个文本位置建议网络 (TLPNet) 和一个文本外观调整网络 (TAANet).
    • TLPNet识别文本嵌入区域,而TAANet调整文本几何和颜色以匹配背景背景.

    主要成果:

    • 拟议的LBTS引擎有效地生成合成场景文本图像.
    • 实验表明,与现有方法相比,LBTS为场景文本检测器产生了优越的预训练数据.

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  • 集成的TLPNet和TAANet成功地将文本实例调整为背景场景.
  • 结论:

    • DecompST数据集和LBTS引擎在场景文本图像合成领域取得了重大进展.
    • 开发的方法可以创建高质量的合成数据,用于训练可靠的场景文本分析模型.
    • 数据集和代码的可用性鼓励在该领域进行进一步的研究和开发.