  Mobile vision transformers (MobileViT) can achieve state-of-the-art
performance across several mobile vision tasks, including classification and
detection. Though these models have fewer parameters, they have high latency as
compared to convolutional neural network-based models. The main efficiency
bottleneck in MobileViT is the multi-headed self-attention (MHA) in
transformers, which requires $O(k^2)$ time complexity with respect to the
number of tokens (or patches) $k$. Moreover, MHA requires costly operations
(e.g., batch-wise matrix multiplication) for computing self-attention,
impacting latency on resource-constrained devices. This paper introduces a
separable self-attention method with linear complexity, i.e. $O(k)$. A simple
yet effective characteristic of the proposed method is that it uses
element-wise operations for computing self-attention, making it a good choice
for resource-constrained devices. The improved model, MobileViTv2, is
state-of-the-art on several mobile vision tasks, including ImageNet object
classification and MS-COCO object detection. With about three million
parameters, MobileViTv2 achieves a top-1 accuracy of 75.6% on the ImageNet
dataset, outperforming MobileViT by about 1% while running $3.2\times$ faster
on a mobile device.
  Our source code is available at: \url{https://github.com/apple/ml-cvnets}
