CAS-ViT:用于高效移动应用的卷积增材自我注意力视觉转换器
概括
视觉变压器 (ViT) 通过卷积增量自我注意 (CAS) 块和卷积增量令牌混合器 (CATM) 提供高效的效率,平衡移动视觉任务的性能和效率.
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:
- 视觉转换器 (ViT) 提供强大的全球背景,但由于复杂的操作,其效率受到限制.
- 像移动设备这样的资源有限的环境需要为实时应用程序优化神经网络架构.
研究的目的:
- 引入CAS-ViT (卷积添加式自我注意力视觉转换器) 以在移动应用中有效部署.
- 开发一种新的卷积增量代币混合器 (CATM),可以降低计算复杂性,同时保持性能.
主要方法:
- 提出了使用空间和通道注意力的卷积增量令牌混合器 (CATM),消除了矩阵乘法和Softmax.
- 引入了包含CATM的卷积添加自我注意 (CAS) 块混合架构.
- 开发了一系列轻量级CAS-ViT网络,可适应各种下游任务.
主要成果:
- 在ImageNet-1K上,CAS-ViT模型 (M和T) 仅在12M/21M参数上实现了83.0%/84.1%的top-1精度.
- 与最先进的骨干相比,在GPU,ONNX和iPhone上显示出更高的吞吐量.
- 在性能,高效推断和易于部署之间实现了有利的平衡.
结论:
- CAS-ViT为移动视觉应用提供了一种高效和高性能替代传统视觉转换器.
- 拟议的CATM模块有效地减少了计算开销,而不会牺牲上下文理解.
- 在资源有限的设备上,CAS-ViT为各种计算机视觉任务提供了通用和可部署的解决方案.
相关概念视频
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The important convolution properties include width, area, differentiation, and integration properties.
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The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
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