# A domain adaptive deep learning solution for scanpath prediction of paintings

arXiv: https://arxiv.org/abs/2209.11338 · HTML: https://mlsanity.com/paper/2209.11338/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1809.00567/) (2018)
- [Kernelized dense layers for facial expression recognition](https://mlsanity.com/paper/2009.10814/) (2020)
- [SALYPATH: A Deep-Based Architecture for visual attention prediction](https://mlsanity.com/paper/2107.00559/) (2021)
- [A Simple and efficient deep Scanpath Prediction](https://mlsanity.com/paper/2112.04610/) (2021)

## Built on by (most cited first, 6 of 6)

- [An Inter-observer consistent deep adversarial training for visual scanpath prediction](https://mlsanity.com/paper/2211.07336/) (2022)
- [What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metric](https://mlsanity.com/paper/2604.08494/) (2026)
- [Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images](https://mlsanity.com/paper/2504.15007/) (2025)
- [SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptation](https://mlsanity.com/paper/2602.22049/) (2026)
- [Divergent Gaze Patterns in Artistic Viewing: Spatial and Temporal Signatures of Attention Across Autistic Individuals, Artists, and Neurotypical Observers](https://mlsanity.com/paper/2607.15227/) (2026)
- [Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans](https://mlsanity.com/paper/2608.16514/) (2026)

## Abstract (arXiv)

Cultural heritage understanding and preservation is an important issue for society as it represents a fundamental aspect of its identity. Paintings represent a significant part of cultural heritage, and are the subject of study continuously. However, the way viewers perceive paintings is strictly related to the so-called HVS (Human Vision System) behaviour. This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. In further details, we introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans, including the fundamental understanding of a scene, and then extend it to painting images. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention. We use an FCNN (Fully Convolutional Neural Network), in which we exploit a differentiable channel-wise selection and Soft-Argmax modules. We also incorporate learnable Gaussian distributions onto the network bottleneck to simulate visual attention process bias in natural scene images. Furthermore, to reduce the effect of shifts between different domains (i.e. natural images, painting), we urge the model to learn unsupervised general features from other domains using a gradient reversal classifier. The results obtained by our model outperform existing state-of-the-art ones in terms of accuracy and efficiency.