# SALYPATH: A Deep-Based Architecture for visual attention prediction

arXiv: https://arxiv.org/abs/2107.00559 · HTML: https://mlsanity.com/paper/2107.00559/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Attention to Scale: Scale-aware Semantic Image Segmentation](https://mlsanity.com/paper/1511.03339/) (2015)
- [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)
- [Human Pose Regression by Combining Indirect Part Detection and Contextual Information](https://mlsanity.com/paper/1710.02322/) (2017)
- [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)
- [Deep Saliency Models : The Quest For The Loss Function](https://mlsanity.com/paper/1907.02336/) (2019)
- [OpenSalicon: An Open Source Implementation of the Salicon Saliency Model](https://mlsanity.com/paper/1606.00110/) (2016)

## Built on by (most cited first, 7 of 7)

- [A domain adaptive deep learning solution for scanpath prediction of paintings](https://mlsanity.com/paper/2209.11338/) (2022)
- [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)
- [What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metric](https://mlsanity.com/paper/2604.08494/) (2026)
- [A Simple and efficient deep Scanpath Prediction](https://mlsanity.com/paper/2112.04610/) (2021)
- [SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptation](https://mlsanity.com/paper/2602.22049/) (2026)
- [OpenVAM: Open-World Visual Attention Modeling with VLMs](https://mlsanity.com/paper/2609.31364/) (2026)

## Abstract (arXiv)

Human vision is naturally more attracted by some regions within their field of view than others. This intrinsic selectivity mechanism, so-called visual attention, is influenced by both high- and low-level factors; such as the global environment (illumination, background texture, etc.), stimulus characteristics (color, intensity, orientation, etc.), and some prior visual information. Visual attention is useful for many computer vision applications such as image compression, recognition, and captioning. In this paper, we propose an end-to-end deep-based method, so-called SALYPATH (SALiencY and scanPATH), that efficiently predicts the scanpath of an image through features of a saliency model. The idea is predict the scanpath by exploiting the capacity of a deep-based model to predict the saliency. The proposed method was evaluated through 2 well-known datasets. The results obtained showed the relevance of the proposed framework comparing to state-of-the-art models.