  Visual saliency prediction using transformers - Convolutional neural networks
(CNNs) have significantly advanced computational modelling for saliency
prediction. However, accurately simulating the mechanisms of visual attention
in the human cortex remains an academic challenge. It is critical to integrate
properties of human vision into the design of CNN architectures, leading to
perceptually more relevant saliency prediction. Due to the inherent inductive
biases of CNN architectures, there is a lack of sufficient long-range
contextual encoding capacity. This hinders CNN-based saliency models from
capturing properties that emulate viewing behaviour of humans. Transformers
have shown great potential in encoding long-range information by leveraging the
self-attention mechanism. In this paper, we propose a novel saliency model that
integrates transformer components to CNNs to capture the long-range contextual
visual information. Experimental results show that the transformers provide
added value to saliency prediction, enhancing its perceptual relevance in the
performance. Our proposed saliency model using transformers has achieved
superior results on public benchmarks and competitions for saliency prediction
models.
  The source code of our proposed saliency model TranSalNet is available at:
https://github.com/LJOVO/TranSalNet
