EPANet-KD:高效的渐进式关注网络通过知识蒸进行细粒度的省级村庄分类
Cheng Zhang1,2, Chunqing Liu1,2, Huimin Gong1,2
1College of Landscape Architecture and Art, Jiangxi Agricultural University, Nanchang, China.
PloS one
|February 15, 2024
概括
这项研究引入了一个新的数据集和一个高效的渐进式关注网络,用于对传统村庄进行分类. EPANet-KD模型平衡了准确性和效率,有助于数字保存.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 保护文化遗产 保护文化遗产
背景情况:
- 传统村庄的细粒度分类对于城市和农村发展至关重要.
- 现有的方法在准确性和效率方面面临着挑战.
研究的目的:
- 提出一个新的数据集,用于对传统村庄的细粒度分类.
- 发展一个高效的渐进式关注网络,以提高分类准确性和效率.
主要方法:
- 引入了传统村庄分类数据集 (PVCD),其中包含4,400张图像.
- 提出了用于空间和通道特征注意的渐进式注意模块 (PAM).
- 开发了一种软化对齐蒸 (SAD) 战略,用于知识转移.
- 创建了EPANet-Teacher和EPANet-Student模型,EPANet-KD使用SAD进行知识蒸.
主要成果:
- EPANet-Teacher实现了67.27%的最先进的准确性.
- EPANet-KD在3.32M参数和0.42G计算方面表现出可比的性能.
- 提出的方法提高了传统村庄分类的精度.
结论:
- EPANet-KD为传统的村庄分类提供了准确性和效率之间的平衡.
- 这项研究旨在促进传统村庄的数字化保护和发展.
- 数据集,代码和结果公开可用,以推进该领域.
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