LIPT:延迟感知图像处理变压器
概括
本研究介绍了LIPT,一个用于图像处理的延迟感知变压器. 在像图像超分辨率这样的任务中,LIPT实现了实际推理加速和最先进的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 变压器模型在图像处理方面越来越有影响力.
- 现有的轻量级变压器优先考虑减少计算 (FLOP) 或参数,而不是实际推断速度.
- 需要对现实世界推理延迟进行优化的图像处理模型.
研究的目的:
- 为了介绍一种新的延迟感知图像处理变压器,LIPT.
- 在图像处理任务中实现实际推理加速.
- 与现有方法相比,提高速度和准确性 (PSNR).
主要方法:
- 引入了低延迟的LIPT块,将自我注意力和卷曲结合起来,以取代内存密集型操作.
- 开发了非挥发性稀疏掩盖自我注意力 (NVSM-SA) 以在更大的窗口上有效地捕获上下文信息.
- 提出了一个高频重定型模块 (HRM),以改进细节重建和模型重定型.
主要成果:
- 在各种图像处理任务中,LIPT在延迟和峰值信号对噪声比率 (PSNR) 上都取得了显著的改进.
- 实现了实时的GPU推断速度.
- 在多个图像超分辨率基准上表现优于现有方法.
结论:
- LIPT为使用变压器加速图像处理任务提供了实用解决方案.
- 拟议的LIPT块,NVSM-SA和HRM有助于高效和有效的图像增强.
- LIPT为使用变压器架构的实时,高性能图像处理设定了新的标准.
相关概念视频
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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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However, if this ratio is less than one, the transformer is said to be a step-down...
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However, if this ratio is less than one, the transformer is said to be a step-down...
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Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality, the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be the high resistance of the...
There are four main reasons for energy losses in transformers.
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