# SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes

arXiv: https://arxiv.org/abs/1707.03123 · HTML: https://mlsanity.com/paper/1707.03123/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2017.

## Built on by (most cited first, 29 of 29)

- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [ScanGAN360: A Generative Model of Realistic Scanpaths for 360circ Images](https://mlsanity.com/paper/2103.13922/) (2021)
- [State-of-the-Art in Human Scanpath Prediction](https://mlsanity.com/paper/2102.12239/) (2021)
- [Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers](https://mlsanity.com/paper/2303.09383/) (2023)
- [Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment](https://mlsanity.com/paper/2305.10983/) (2023)
- [GazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpaths](https://mlsanity.com/paper/2408.02788/) (2024)
- [Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360deg Omnidirectional Image](https://mlsanity.com/paper/2212.12378/) (2022)
- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [Beyond Average: Individualized Visual Scanpath Prediction](https://mlsanity.com/paper/2404.12235/) (2024)
- [EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning](https://mlsanity.com/paper/2404.10163/) (2024)
- [SALYPATH: A Deep-Based Architecture for visual attention prediction](https://mlsanity.com/paper/2107.00559/) (2021)
- [Scanpath Prediction on Information Visualisations](https://mlsanity.com/paper/2112.02340/) (2021)
- [Perceptual Quality Assessment of 360^circ Images Based on Generative Scanpath Representation](https://mlsanity.com/paper/2309.03472/) (2023)
- [Few-shot Personalized Scanpath Prediction](https://mlsanity.com/paper/2504.05499/) (2025)
- [DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural Images](https://mlsanity.com/paper/2509.16767/) (2025)
- [SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images](https://mlsanity.com/paper/2201.00096/) (2022)
- [Improving saliency models' predictions of the next fixation with humans' intrinsic cost of gaze shifts](https://mlsanity.com/paper/2207.04250/) (2022)
- [Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization](https://mlsanity.com/paper/2305.02536/) (2023)
- [How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model](https://mlsanity.com/paper/2408.05411/) (2024)
- [TPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processes](https://mlsanity.com/paper/2410.23409/) (2024)
- [A Probabilistic Time-Evolving Approach to Scanpath Prediction](https://mlsanity.com/paper/2204.09404/) (2022)
- [Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task](https://mlsanity.com/paper/2212.10367/) (2022)
- [DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360deg Images](https://mlsanity.com/paper/2403.17477/) (2024)
- [A Simple and efficient deep Scanpath Prediction](https://mlsanity.com/paper/2112.04610/) (2021)
- [Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors](https://mlsanity.com/paper/2404.10836/) (2024)
- [SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptation](https://mlsanity.com/paper/2602.22049/) (2026)
- [Pathformer3D: A 3D Scanpath Transformer for 360deg Images](https://mlsanity.com/paper/2407.10563/) (2024)
- [Visual Attention Graph](https://mlsanity.com/paper/2503.08531/) (2025)
- [A Comparative Study of Scanpath Models in Graph-Based Visualization](https://mlsanity.com/paper/2503.24160/) (2025)

## Abstract (arXiv)

We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The model is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at https://github.com/massens/saliency-360salient-2017.