单词自动更新对比的对抗网络用于文本到图像合成
Jian Xiao1, Yiwen Sun2, Xiaojun Bi3
1College of Information and Communication Engineering, Harbin Engineering University, 150001, Harbin, China.
概括
本研究介绍了Word自动更新对比对抗网络 (WSC-GAN),用于从文本中实现现实的细粒度图像合成. WSC-GAN框架通过专注于重要单词并确保语义一致性来改善文本到图像的生成.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 生成高质量的,与文本一致的细粒度图像是一个具有挑战性的计算机视觉问题.
- 现有的生成对抗网络 (GAN) 由于固定的词特性和不充分的语义一致性学习,在文本到图像合成方面扎.
研究的目的:
- 提出一种新的注意力生成和对比的对抗框架,用于细粒度的文本到图像合成.
- 通过改进单词重要性利用和语义一致性来解决现有GAN的局限性.
主要方法:
- 引入了Word自我更新的对比对抗网络 (WSC-GAN).
- 整合了双重注意模块,用于颜色和语义细节建模.
- 为生成器开发了一个单词自动更新模块,以利用视觉上重要的单词.
- 使用了多分支对比区分器,对图像句子和图像词一致性产生了新的对比损失.
主要成果:
- 拟议的WSC-GAN框架在细粒度文本到图像合成中表现出更好的性能.
- 与最先进的方法相比,对CUB和MS-COCO数据集的实验显示出更优异的结果.
- 双重注意力和单词自动更新模块有效地提高了图像质量和文本对齐.
结论:
- WSC-GAN在细粒度文本到图像合成中提供了显著的进步.
- 该方法有效地解决了生成模型中单词重要性和语义一致性的问题.
- 该框架显示出从文本描述中生成现实和准确的图像的巨大潜力.
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