  Convolutional layers are an integral part of many deep neural network
solutions in computer vision. Recent work shows that replacing the standard
convolution operation with mechanisms based on self-attention leads to improved
performance on image classification and object detection tasks. In this work,
we show how attention mechanisms can be used to replace another canonical
operation: strided transposed convolution. We term our novel attention-based
operation attention-based upsampling since it increases/upsamples the spatial
dimensions of the feature maps. Through experiments on single image
super-resolution and joint-image upsampling tasks, we show that attention-based
upsampling consistently outperforms traditional upsampling methods based on
strided transposed convolution or based on adaptive filters while using fewer
parameters. We show that the inherent flexibility of the attention mechanism,
which allows it to use separate sources for calculating the attention
coefficients and the attention targets, makes attention-based upsampling a
natural choice when fusing information from multiple image modalities.
