循环MLP:一种类似于MLP的架构,用于密集的视觉预测
概括
CycleMLP是一个新的多层感知器 (MLP) 架构,旨在进行密集的视觉预测. 它有效地处理各种图像大小,并实现线性计算复杂性,优于现有模型.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 多层感知器 (MLP) 在密集的视觉预测任务中存在局限性,原因是对图像大小和二次计算复杂性的敏感性.
- 现有的先进的MLP架构,如MLP-Mixer,ResMLP和gMLP,对于密集的预测任务通常是不可行的.
- 卷积神经网络 (CNN) 和变压器占主导地位,但可能是计算密集的.
研究的目的:
- 介绍CycleMLP,一个通用的神经骨干网络,用于密集的视觉预测任务.
- 解决现有的MLP架构在图像大小适应性和计算效率方面的局限性.
- 提供一个理论分析,比较CycleMLP与卷积和自我注意力机制.
主要方法:
- 开发了CycleMLP,一种新的MLP架构,利用本地窗口进行高效的计算.
- 在图像大小方面实现了线性计算复杂性,与传统MLP的O(N^2) 复杂性形成鲜明对比.
- 进行理论分析,以了解CycleMLP,卷积和多头自我注意力之间的关系.
主要成果:
- 循环MLP证明了适应各种空间图像大小的适应性.
- 实现了线性计算复杂性,使其适合大规模的密集预测任务.
- 使用CycleMLP构建的模型在性能上超过了最先进的MLP和变压器模型,使用的参数和FLOP更少.
- 在ADE20 K数据集上,CycleMLP-Tiny的表现比Swin-Tiny优于1.3%mIoU.
- 在ImageNet-C数据集上表现出卓越的零射击稳定性.
结论:
- CycleMLP提供了一个多功能和高效的骨干,用于密集的视觉预测任务,如对象检测,细分和人类姿势估计.
- 该架构克服了以前的MLP的局限性,使其在计算机视觉中具有更广泛的适用性.
- 循环MLP为变压器模型提供了有竞争力的替代方案,可以在降低计算成本的情况下获得优异或可比的结果.
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