# SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images

arXiv: https://arxiv.org/abs/2201.00096 · HTML: https://mlsanity.com/paper/2201.00096/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 1 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Human Pose Regression by Combining Indirect Part Detection and Contextual Information](https://mlsanity.com/paper/1710.02322/) (2017)
- [Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1710.10755/) (2017)
- [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)
- [ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos](https://mlsanity.com/paper/2011.10600/) (2020)
- [Streaming Virtual Reality Content](https://mlsanity.com/paper/1612.08350/) (2016)

## Built on by (most cited first, 4 of 4)

- [Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization](https://mlsanity.com/paper/2305.02536/) (2023)
- [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)
- [Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans](https://mlsanity.com/paper/2608.16514/) (2026)

## Abstract (arXiv)

This paper introduces a new framework to predict visual attention of omnidirectional images. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generate representative saliency maps. In addition, an auxiliary network is employed to generate probable viewport center fixation points through the SoftArgMax function. The latter allows to derive fixation points from feature maps. To take advantage of the scanpath prediction, an adaptive joint probability distribution model is then applied to construct the final unbiased saliency map by leveraging the encoder decoder-based saliency map and the scanpath-based saliency heatmap. The proposed framework was evaluated in terms of saliency and scanpath prediction, and the results were compared to state-of-the-art methods on Salient360! dataset. The results showed the relevance of our framework and the benefits of such architecture for further omnidirectional visual attention prediction tasks.