语义面具重建和类别语义学习用于少数镜头图像生成
Ting Xiao1, Yunjie Cai2, Jiaoyan Guan2
1Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, 200237, China; Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.
概括
本研究介绍了语义面具重建 (SMR) 和分类语义学习 (CSL),以提高少数镜头图像生成质量和多样性. 新的SMR-CSL方法增强语义理解,以更好的图像合成和下游任务协助.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 少数拍摄图像生成旨在使用有限的示例为未见的类别创建新型图像.
- 由于语义理解和表示提取方面的挑战,当前的方法难以生成高质量和多样化的图像.
研究的目的:
- 提出一种新的方法,语义面具重建 (SMR) 和分类语义学习 (CSL),以增强少数镜头图像生成.
- 为了提高产生的图像的质量,多样性和真实性,在少数拍摄的学习场景中.
主要方法:
- 语义面具重建 (SMR):在语义空间中执行面具重建,以动态调整的面具比率来增强区分器学习.
- 类别语义学习 (CSL):利用三位数的损失来优化类别间的距离,改善细粒度生成.
- 无论是SMR还是CSL,都被设计成插即用模块.
主要成果:
- 拟议的SMR-CSL方法在生成三种标准数据集中更高质量和更多样化的图像方面明显优于现有的方法.
- 下游分类实验证实了SMR-CSL生成的图像在协助分类任务中的有效性.
结论:
- SMR和CSL有效地解决了当前少数镜头图像生成技术的局限性.
- 该SMR-CSL方法提供了一个强大的和多功能解决方案,用于生成语义丰富和多样化的图像,在下游任务中有实际应用.
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