分因分辨视觉变压器:用局部窗口成本建模远程依赖性
IEEE transactions on neural networks and learning systems
|December 28, 2023
概括
新的因子化视觉转换器 (FaViT) 提供了一种新的自我注意力机制,以提高计算机视觉中的计算效率和全球依赖性建模. 与Swin变压器等现有模型相比,这种方法提高了性能和稳定性.
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.
- 人工智能的人工智能
背景情况:
- 变压器提供了强大的图像表示,但面临着计算挑战,通过分辨率进行二次缩放.
- 斯温变压器使用本地窗口来减少计算,但限制了全球依赖性建模和稳定性.
研究的目的:
- 开发一种能平衡计算成本,提高性能和稳定性的视觉变压器.
- 克服局部基于窗口的自我注意的局限性,以建模全球依赖,并确保模型的稳定性.
主要方法:
- 通过将注意力矩阵分解为稀疏的次注意力矩阵,提出了一种新的因子化自我注意力 (FaSA) 机制.
- 开发了因子化视觉转换器 (FaViT),这是利用FaSA机制的层次模型.
- 在图像分辨率方面实现了线性计算复杂性.
主要成果:
- 在图像分类和下游任务中,FaViT表现出高性能.
- 该模型对受损和有偏见的数据表现出显著的稳定性.
- 与Swin-T相比,FaViT-B2比Swin-T提高了1%的分类精度和7%的稳定性,参数减少了14%.
结论:
- 在计算成本和性能之间,FaSA提供了有效的权衡,使得远程依赖性建模成为可能.
- 对于实际的计算机视觉应用,FaViT提供了一个强大的,高效的替代方案.
- 拟议的方法显示了在视觉任务中推进变压器架构的前景.
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