PEIPNet:具有深度和点向卷积的参数高效图像绘制网络
Jaekyun Ko1, Wanuk Choi1, Sanghwan Lee1
1Department of Mechanical Convergence Engineering, Hanyang University, Seoul 04763, Republic of Korea.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
我们介绍了一个参数有效的图像绘制网络 (PEIPNet),可以减少计算负载,以实现有效的图像绘制. 这种新的方法在运行内存最小的情况下实现了最先进的结果.
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.
- 图像处理 图像处理
背景情况:
- 当前的图像绘制研究往往会增加模型的复杂性和资源需求.
- 这一趋势给计算效率和实际应用带来了挑战.
研究的目的:
- 提出一个参数高效的图像绘制网络 (PEIPNet),以实现高效和有效的图像绘制.
- 为了解决与传统的油漆方法相关的计算负担.
主要方法:
- 开发了一个单阶段的inpainting框架,利用深度和点向的卷积来最大限度地降低参数和计算成本.
- 集成的空间适应性非规范化 (SPADE) 用于面具条件规范化,密度扩展卷积模块 (DDCM) 用于防止梯度消失和捕获全球上下文,以及高效的自我注意力 (ESA) 用于远程信息提取.
主要成果:
- 与现有的最先进的方法相比,PEIPNet的操作内存使用率显著降低.
- 定性和定量实验证实了该模型在各种数据集 (巴黎街景,CelebA,Places2) 中具有普遍的绘制功能.
结论:
- 对于图像绘制任务,PEIPNet提供了一种高效有效的解决方案.
- 拟议的架构成功地平衡了性能与减少计算资源需求.
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