# SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptation

arXiv: https://arxiv.org/abs/2602.22049 · HTML: https://mlsanity.com/paper/2602.22049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 66 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [Unified Image and Video Saliency Modeling](https://mlsanity.com/paper/2003.05477/) (2020)
- [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1701.01081/) (2017)
- [Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet](https://mlsanity.com/paper/1411.1045/) (2014)
- [DeepGaze II: Reading fixations from deep features trained on object recognition](https://mlsanity.com/paper/1610.01563/) (2016)
- [A Deep Multi-Level Network for Saliency Prediction](https://mlsanity.com/paper/1609.01064/) (2016)
- [TranSalNet: Towards perceptually relevant visual saliency prediction](https://mlsanity.com/paper/2110.03593/) (2021)
- [Shallow and Deep Convolutional Networks for Saliency Prediction](https://mlsanity.com/paper/1603.00845/) (2016)
- [PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1809.00567/) (2018)
- [SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes](https://mlsanity.com/paper/1707.03123/) (2017)
- [SALYPATH: A Deep-Based Architecture for visual attention prediction](https://mlsanity.com/paper/2107.00559/) (2021)
- [A domain adaptive deep learning solution for scanpath prediction of paintings](https://mlsanity.com/paper/2209.11338/) (2022)
- [Domain-adversarial Network Alignment](https://mlsanity.com/paper/1908.05429/) (2019)
- [Deep-based quality assessment of medical images through domain adaptation](https://mlsanity.com/paper/2210.10533/) (2022)
- [An Inter-observer consistent deep adversarial training for visual scanpath prediction](https://mlsanity.com/paper/2211.07336/) (2022)
- [Simulating Human Gaze with Neural Visual Attention](https://mlsanity.com/paper/2211.12100/) (2022)

## Built on by (most cited first, 1 of 1)

- [Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans](https://mlsanity.com/paper/2608.16514/) (2026)

## 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.