# Few-shot Personalized Saliency Prediction Based on Interpersonal Gaze Patterns

arXiv: https://arxiv.org/abs/2307.02799 · HTML: https://mlsanity.com/paper/2307.02799/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Salient Object Detection: A Survey](https://mlsanity.com/paper/1411.5878/) (2014)
- [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1701.01081/) (2017)
- [Contextual Encoder-Decoder Network for Visual Saliency Prediction](https://mlsanity.com/paper/1902.06634/) (2019)
- [Predicting Visual Importance Across Graphic Design Types](https://mlsanity.com/paper/2008.02912/) (2020)
- [Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition](https://mlsanity.com/paper/1702.04710/) (2017)
- [Tensor-on-tensor regression](https://mlsanity.com/paper/1701.01037/) (2017)
- [Personalized Saliency and its Prediction](https://mlsanity.com/paper/1710.03011/) (2017)
- [Where's YOUR focus: Personalized Attention](https://mlsanity.com/paper/1802.07931/) (2018)
- [Clustered Saliency Prediction](https://mlsanity.com/paper/2207.02205/) (2022)

## Abstract (arXiv)

This study proposes a few-shot personalized saliency prediction method that leverages interpersonal gaze patterns. Unlike general saliency maps, personalized saliency maps (PSMs) capture individual visual attention and provide insights into individual visual preferences. However, predicting PSMs is challenging because of the complexity of gaze patterns and the difficulty of collecting extensive eye-tracking data from individuals. An effective strategy for predicting PSMs from limited data is the use of eye-tracking data from other persons. To efficiently handle the PSMs of other persons, this study focuses on the selection of images to acquire eye-tracking data and the preservation of the structural information of PSMs. In the proposed method, these images are selected such that they bring more diverse gaze patterns to persons, and structural information is preserved using tensor-based regression. The experimental results demonstrate that these two factors are beneficial for few-shot PSM prediction.