Light-weight convolutional neural networks (CNNs) are the de-facto for mobile
vision tasks. Their spatial inductive biases allow them to learn
representations with fewer parameters across different vision tasks. However,
these networks are spatially local. To learn global representations,
self-attention-based vision trans-formers (ViTs) have been adopted. Unlike
CNNs, ViTs are heavy-weight. In this paper, we ask the following question: is
it possible to combine the strengths of CNNs and ViTs to build a light-weight
and low latency network for mobile vision tasks? Towards this end, we introduce
MobileViT, a light-weight and general-purpose vision transformer for mobile
devices. MobileViT presents a different perspective for the global processing
of information with transformers, i.e., transformers as convolutions. Our
results show that MobileViT significantly outperforms CNN- and ViT-based
networks across different tasks and datasets. On the ImageNet-1k dataset,
MobileViT achieves top-1 accuracy of 78.4% with about 6 million parameters,
which is 3.2% and 6.2% more accurate than MobileNetv3 (CNN-based) and DeIT
(ViT-based) for a similar number of parameters. On the MS-COCO object detection
task, MobileViT is 5.7% more accurate than MobileNetv3 for a similar number of
parameters.
Our source code is open-source and available at:
this https URL

本文提出了一种 MobileViT 轻量级通用视觉变换器，将 transformers 视为卷积，可用于移动设备，取得了比 CNN 和 ViT 更好的性能，特别是在对象检测任务上。

MobileViT：轻量级、通用、面向移动的视觉 Transformer

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision  Transformer

This paper proposes a new light-weight convolutional neural network (5k
parameters) for non-uniform illumination image enhancement to handle color,
exposure, contrast, noise and artifacts, etc., simultaneously and effectively.
More concretely, the input image is first enhanced using Retinex model from
dual different aspects (enhancing under-exposure and suppressing
over-exposure), respectively. Then, these two enhanced results and the original
image are fused to obtain an image with satisfactory brightness, contrast and
details. Finally, the extra noise and compression artifacts are removed to get
the final result. To train this network, we propose a semi-supervised
retouching solution and construct a new dataset (82k images) contains various
scenes and light conditions. Our model can enhance 0.5 mega-pixel (like
600*800) images in real time (50 fps), which is faster than existing
enhancement methods. Extensive experiments show that our solution is fast and
effective to deal with non-uniform illumination images.

本文提出一种新的轻量级卷积神经网络（5k 参数），用于处理各种颜色、曝光、对比度、噪点和伪影等问题，实现非均匀照明图像增强，并通过半监督的修复方法，结合新数据集进行训练。该模型可在实时环境下（50 fps）快速增强图片，有效处理非均匀照明图像。