# Personalized Saliency and its Prediction

arXiv: https://arxiv.org/abs/1710.03011 · HTML: https://mlsanity.com/paper/1710.03011/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2017.

## Built on by (most cited first, 13 of 13)

- [Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation](https://mlsanity.com/paper/2101.09014/) (2021)
- [Beyond Average: Individualized Visual Scanpath Prediction](https://mlsanity.com/paper/2404.12235/) (2024)
- [Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services](https://mlsanity.com/paper/2208.00369/) (2022)
- [Few-shot Personalized Scanpath Prediction](https://mlsanity.com/paper/2504.05499/) (2025)
- [Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks](https://mlsanity.com/paper/2505.02413/) (2025)
- [Learning User Embeddings from Human Gaze for Personalised Saliency Prediction](https://mlsanity.com/paper/2403.13653/) (2024)
- [Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction](https://mlsanity.com/paper/2109.08371/) (2021)
- [Context-empowered Visual Attention Prediction in Pedestrian Scenarios](https://mlsanity.com/paper/2210.16933/) (2022)
- [Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception](https://mlsanity.com/paper/2112.13697/) (2021)
- [Clustered Saliency Prediction](https://mlsanity.com/paper/2207.02205/) (2022)
- [Few-shot Personalized Saliency Prediction Based on Interpersonal Gaze Patterns](https://mlsanity.com/paper/2307.02799/) (2023)
- [PRE-MAP: Personalized Reinforced Eye-tracking Multimodal LLM for High-Resolution Multi-Attribute Point Prediction](https://mlsanity.com/paper/2507.19213/) (2025)
- [Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation](https://mlsanity.com/paper/2604.09368/) (2026)

## Abstract (arXiv)

Nearly all existing visual saliency models by far have focused on predicting a universal saliency map across all observers. Yet psychology studies suggest that visual attention of different observers can vary significantly under specific circumstances, especially a scene is composed of multiple salient objects. To study such heterogenous visual attention pattern across observers, we first construct a personalized saliency dataset and explore correlations between visual attention, personal preferences, and image contents. Specifically, we propose to decompose a personalized saliency map (referred to as PSM) into a universal saliency map (referred to as USM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized saliency. We then present two solutions towards predicting such discrepancy maps, i.e., a multi-task convolutional neural network (CNN) framework and an extended CNN with Person-specific Information Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the effectiveness of our models for PSM prediction as well their generalization capability for unseen observers.