We present a novel method for saliency prediction that leverages a cognitive model of visual attention as an inductive bias. This approach is in stark contrast to recent purely data-driven saliency models that achieve performance improvements mainly by increased capacity, resulting in high computational costs and the need for large-scale training datasets. We demonstrate that by using a cognitive model, our method achieves competitive performance to the state of the art across several natural image datasets while only requiring a fraction of the parameters. Furthermore, we set the new state of the art for saliency prediction on information visualizations, demonstrating the effectiveness of our approach for cross-domain generalization. We further provide augmented versions of the full MSCOCO dataset with synthetic gaze data using the cognitive model, which we used to pre-train our method. Our results are highly promising and underline the significant potential of bridging between cognitive and data-driven models, potentially also beyond attention.

@inproceedings{sood23_cogsci,
  title = {Improving Neural Saliency Prediction with a Cognitive Model of Human Visual Attention},
  author = {Ekta Sood and Lei Shi and Matteo Bortoletto and Yao Wang and Philipp Müller and Andreas Bulling},
  year = {2023},
  booktitle = {Proc. Annual Meeting of the Cognitive Science Society (CogSci)},
  pages = {3639--3646},
}