%0 Conference Paper %1 sood23_cogsci %A Sood, Ekta %A Shi, Lei %A Bortoletto, Matteo %A Wang, Yao %A Müller, Philipp %A Bulling, Andreas %B Proc. the 45th Annual Meeting of the Cognitive Science Society (CogSci) %D 2023 %K exc2075 pn7 pn7-5 updated %P 3639--3646 %T Improving Neural Saliency Prediction with a Cognitive Model of Human Visual Attention %X 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, abstract = {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.}, added-at = {2025-02-17T14:54:07.000+0100}, author = {Sood, Ekta and Shi, Lei and Bortoletto, Matteo and Wang, Yao and Müller, Philipp and Bulling, Andreas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2116525f8369c3bf70a00a497ae4363ef/hermann}, booktitle = {Proc. the 45th Annual Meeting of the Cognitive Science Society (CogSci)}, code = {https://git.hcics.simtech.uni-stuttgart.de/public-projects/neural-saliency-prediction-with-a-cognitive-model/}, dataset = {https://perceptualui.org/research/datasets/MSCOCOEMMAFigureQAEMMA/}, interhash = {9039d069dd9d3f940693c0f44272cb3f}, intrahash = {116525f8369c3bf70a00a497ae4363ef}, keywords = {exc2075 pn7 pn7-5 updated}, month = {July}, note = {spotlight}, pages = {3639--3646}, supp = {Yes}, timestamp = {2025-02-17T14:54:07.000+0100}, title = {Improving Neural Saliency Prediction with a Cognitive Model of Human Visual Attention}, year = 2023 }