QWNet:为空间频率意识的多模态图像融合提供一个四分离子波束网络
Jietao Yang1, Miaoshan Lin1, Guoheng Huang1
1Guangdong University of Technology, Guangzhou, 510006, Guangdong Province, China.
概括
QWNet是一个新的四边形波浪网络,通过集成频率和空间信息来改进多模式图像融合. 这种方法增强了视觉任务,如语义细分,具有卓越的融合质量和效率.
科学领域:
- 计算机视觉 计算机视觉
- 信号处理 信号处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 多模态图像融合 (MMIF) 结合了图像模式,以增强视觉任务.
- 现有的MMIF方法缺乏频域意识,忽视频道间的关系.
- 挑战包括自适应融合和模拟复杂的依赖关系.
研究的目的:
- 建议QWNet,一个用于增强MMIF的四边形波形网络.
- 解决现有的频域和频道组合技术的局限性.
- 提高对象的可见性,纹理细节和下游任务性能.
主要方法:
- 使用波形变换进行空间和频率分解.
- 将组件表示为四次元,以建模复杂的通道间依赖关系.
- 介绍双向自适应注意模块 (BAAM) 和四交叉模式融合模块 (QCFM).
主要成果:
- 与现有方法相比,QWNet显示出优越的融合质量.
- 在下游任务中实现了最先进的性能,例如语义细分.
- 效率只有4.27K参数和0.30G的FLOP.
结论:
- QWNet有效地利用MMIF的空间和频率信息.
- 拟议的模块增强了功能交互和融合.
- 对于先进的视觉任务,QWNet提供了一个有希望的,高效的解决方案.
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