MULLER: Multilayer Laplacian Resizer for Vision
Zhengzhong Tu, Peyman Milanfar, Hossein Talebi
[Google Research]
MULLER: 面向视觉的多层拉普拉斯缩放器
要点:
- 动机：深度学习中，图像缩放方法的前端缩放器会影响深度视觉模型的性能，现有方法受限于常见的可用缩放器，尚未探究其它缩放器的潜力。
- 方法：提出一种基于多层拉普拉斯分解的图像缩放方法MULLER，能以极低的成本有效地提高深度视觉任务的性能。
- 优势：与现有方法相比，MULLER具有更高的性能和更高的推理效率；可以轻松地应用于不同的训练流程和多种深度视觉任务中，包括图像分类、目标检测和分割、图像质量评估等。
一句话总结:
MULLER是一种轻量的、基于多层拉普拉斯分解的图像缩放方法，能有效提高图像识别模型的性能和推理效率。
https://arxiv.org/abs/2304.02859 
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