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多模式图像合成和编辑:生成性AI时代
IEEE transactions on pattern analysis and machine intelligence
|August 25, 2023
概括
本次调查探讨了多式联机图像合成和编辑,重点关注不同数据类型如何相互作用. 它详细介绍了当前的方法,挑战和计算机视觉领域的未来研究方向.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 多式联络AI 多式联络AI
背景情况:
- 多式联运信息的有效交互和融合对于创建和感知数据至关重要.
- 多模式图像合成和编辑利用这种互动,成为一个重要的研究领域.
- 多模式指导提供灵活的图像操纵,没有明确的网络培训.
研究的目的:
- 综合审查近期在多式联动图像合成和编辑方面的进展.
- 根据数据模式和模型类型对方法进行分类.
- 确定当前的挑战和未来的研究途径.
主要方法:
- 按数据模式和模型架构对多式图像合成和编辑技术的分类.
- 审查在图像合成和编辑中使用的不同指导模式.
- 对基准数据集,评估指标和实验结果的分析.
主要成果:
- 一个结构化的概述当前在多模式图像合成和编辑的最先进的状态.
- 确定关键挑战,包括特征对齐,高分辨率合成和评估指标.
- 分类学是基于数据模式和模型类型来制定的.
结论:
- 多模式图像合成和编辑显示出很大的希望,但在特征对齐和评估方面面临挑战.
- 未来的研究应该专注于解决这些挑战,以推动该领域的发展.
- 该调查为多式联络人工智能和计算机视觉领域的研究人员提供了路线图.
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