SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptation
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Abstract (arXiv)
Understanding human visual attention is key to preserving cultural heritage We introduce SPGen a novel deep learning model to predict scanpaths the sequence of eye movementswhen viewers observe paintings. Our architecture uses a Fully Convolutional Neural Network FCNN with differentiable fixation selection and learnable Gaussian priors to simulate natural viewing biases To address the domain gap between photographs and artworks we employ unsupervised domain adaptation via a gradient reversal layer allowing the model to transfer knowledge from natural scenes to paintings Furthermore a random noise sampler models the inherent stochasticity of eyetracking data. Extensive testing shows SPGen outperforms existing methods offering a powerful tool to analyze gaze behavior and advance the preservation and appreciation of artistic treasures.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 66 references that are in the map. First posted on arXiv 25 Feb 2026.
Builds on
18 of 66 references.
References · 18
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- 2018PathGAN: Visual Scanpath Prediction with Generative Adversarial Networkscited by 32
- 2017SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumescited by 29
6 more
- 2021SALYPATH: A Deep-Based Architecture for visual attention predictioncited by 7
- 2022A domain adaptive deep learning solution for scanpath prediction of paintingscited by 6
- 2019Domain-adversarial Network Alignmentcited by 4
- 2022Deep-based quality assessment of medical images through domain adaptationcited by 4
- 2022An Inter-observer consistent deep adversarial training for visual scanpath predictioncited by 2
- 2022Simulating Human Gaze with Neural Visual Attentioncited by 1
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Citing papers per year: 2026: 1.
1 paper.