# An Inter-observer consistent deep adversarial training for visual scanpath prediction

arXiv: https://arxiv.org/abs/2211.07336 · HTML: https://mlsanity.com/paper/2211.07336/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 14 Nov 2022.

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

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1809.00567/) (2018)
- [ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos](https://mlsanity.com/paper/2011.10600/) (2020)
- [Deep Saliency Models : The Quest For The Loss Function](https://mlsanity.com/paper/1907.02336/) (2019)
- [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)
- [SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images](https://mlsanity.com/paper/2201.00096/) (2022)
- [Deep-based quality assessment of medical images through domain adaptation](https://mlsanity.com/paper/2210.10533/) (2022)

## Built on by (most cited first, 2 of 2)

- [SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptation](https://mlsanity.com/paper/2602.22049/) (2026)
- [Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans](https://mlsanity.com/paper/2608.16514/) (2026)

## Abstract (arXiv)

The visual scanpath is a sequence of points through which the human gaze moves while exploring a scene. It represents the fundamental concepts upon which visual attention research is based. As a result, the ability to predict them has emerged as an important task in recent years. In this paper, we propose an inter-observer consistent adversarial training approach for scanpath prediction through a lightweight deep neural network. The adversarial method employs a discriminative neural network as a dynamic loss that is better suited to model the natural stochastic phenomenon while maintaining consistency between the distributions related to the subjective nature of scanpaths traversed by different observers. Through extensive testing, we show the competitiveness of our approach in regard to state-of-the-art methods.