Abstract Vision Transformers (ViT) have shown rapid progress in computer vision tasks,
achieving promising results on various benchmarks. However, due to the massive number of …

With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to
optimize the performance and complexity of ViTs to enable efficient deployment on mobile …

Self-attention based models such as vision transformers (ViTs) have emerged as a very
competitive architecture alternative to convolutional neural networks (CNNs) in computer …

Abstract Recently lightweight Vision Transformers (ViTs) demonstrate superior performance
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …

The recent amalgamation of transformer and convolutional designs has led to steady
improvements in accuracy and efficiency of the models. In this work, we introduce FastViT, a …

Vision transformers have shown great success due to their high model capabilities.
However, their remarkable performance is accompanied by heavy computation costs, which …

In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are
usually developed. Such models demand high computational resources and therefore …

Convolution neural networks (CNNs) have long been dominating the model choice in on-
device intelligent mobile applications. Recently, we are witnessing the fast development of …

Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across
several mobile vision tasks, including classification and detection. Though these models …

This paper studies the efficiency problem for visual transformers by excavating redundant
calculation in given networks. The recent transformer architecture has demonstrated its …