  The main contributions of our work are two-fold. First, we present a
Self-Attention MobileNet, called SA-MobileNet Network that can model long-range
dependencies between the image features instead of processing the local region
as done by standard convolutional kernels. SA-MobileNet contains self-attention
modules integrated with the inverted bottleneck blocks of the MobileNetV3 model
which results in modeling of both channel-wise attention and spatial attention
of the image features and at the same time introduce a novel self-attention
architecture for low-resource devices. Secondly, we propose a novel training
pipeline for the task of image tilt detection. We treat this problem in a
multi-label scenario where we predict multiple angles for a tilted input image
in a narrow interval of range 1-2 degrees, depending on the dataset used. This
process induces an implicit correlation between labels without any
computational overhead of the second or higher-order methods in multi-label
learning. With the combination of our novel approach and the architecture, we
present state-of-the-art results on detecting the image tilt angle on mobile
devices as compared to the MobileNetV3 model. Finally, we establish that
SA-MobileNet is more accurate than MobileNetV3 on SUN397, NYU-V1, and ADE20K
datasets by 6.42%, 10.51%, and 9.09% points respectively, and faster by at
least 4 milliseconds on Snapdragon 750 Octa-core.
