冰:隐式坐标编码器用于多个图像神经表示
概括
隐式神经表示 (INR) 是用于图像任务的先进技术. 这项研究引入了一个隐式坐标编码器 (ICE),以显著减少图像集合和大图像的模型大小,提高效率.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 隐式神经表示 (INRs) 与表示像图像和形状这样的信号的离散方法相比具有优势.
- 将INR扩展到图像集合是具有挑战性的,因为参数要求快速增长.
- 现有的INR方法主要依赖于多层感知子 (MLP).
研究的目的:
- 为INR提出一个完全隐含的方法,在多个图像表示任务中大幅减少模型大小.
- 通过学习一个共同的特征空间,引入隐式坐标编码器 (ICE) 来有效地缩放具有图像数的INR.
- 为了证明该方法对图像集合和大 (千兆像素) 图像的适用性.
主要方法:
- 开发了一种完全隐式的INR方法,使用单个ICE (编码器) 和多个MLP (解码器) 的自动编码器架构.
- 在ICE和MLP的联合培训中采用多任务学习策略.
- 实现了ICE作为一维卷积编码器,首次将卷积块集成到INR网络中.
- 在处理大型图像时采用了"分裂与征服"的策略.
主要成果:
- 实现了对多个图像表示任务的MLP模型大小的显著减少.
- 通过通过ICE学习一个共同的特征空间,证明了对图像集合的INR有效扩展.
- 展示了该方法对使用"分裂与征服"方法的大单图像的有效性.
- 获得了比以前完全隐含的方法更好的质量,在柯达数据集上减少了多达50%的参数,并获得了大型的冥王星图像.
结论:
- 拟议的ICE方法提供了一个可扩展和高效的解决方案,用于将INR应用于图像集合和大型图像.
- 通过ICE将卷积块集成到INR网络中,可以提高性能并减少参数数量.
- 这项工作开创了INR架构中卷积元件的使用,推进了隐式神经表示领域.
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