# Enhancing Saliency Prediction in Monitoring Tasks: The Role of Visual Highlights

arXiv: https://arxiv.org/abs/2405.09695 · HTML: https://mlsanity.com/paper/2405.09695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 4 of 4)

- [TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection](https://mlsanity.com/paper/1908.05786/) (2019)
- [TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction](https://mlsanity.com/paper/2301.02315/) (2023)
- [Tidying Deep Saliency Prediction Architectures](https://mlsanity.com/paper/2003.04942/) (2020)
- [Saliency Prediction for Mobile User Interfaces](https://mlsanity.com/paper/1711.03726/) (2017)

## Built on by (most cited first, 1 of 1)

- [Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlighting](https://mlsanity.com/paper/2602.08403/) (2026)

## Abstract (arXiv)

This study examines the role of visual highlights in guiding user attention in drone monitoring tasks, employing a simulated interface for observation. The experiment results show that such highlights can significantly expedite the visual attention on the corresponding area. Based on this observation, we leverage both the temporal and spatial information in the highlight to develop a new saliency model: the highlight-informed saliency model (HISM), to infer the visual attention change in the highlight condition. Our findings show the effectiveness of visual highlights in enhancing user attention and demonstrate the potential of incorporating these cues into saliency prediction models.