# Prediction of Search Targets From Fixations in Open-World Settings

arXiv: https://arxiv.org/abs/1502.05137 · HTML: https://mlsanity.com/paper/1502.05137/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2015.

## Built on by (most cited first, 6 of 6)

- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [Neural Photofit: Gaze-based Mental Image Reconstruction](https://mlsanity.com/paper/2108.07524/) (2021)
- [Vocabulary-informed Zero-shot and Open-set Learning](https://mlsanity.com/paper/2301.00998/) (2023)
- [Distinguishing Target and Non-Target Fixations with EEG and Eye Tracking in Realistic Visual Scenes](https://mlsanity.com/paper/2508.01853/) (2025)
- [Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction](https://mlsanity.com/paper/2508.01860/) (2025)
- [Gaze-to-text Generation: Beyond Categorical Decoding of Human Attention](https://mlsanity.com/paper/2607.23917/) (2026)

## Abstract (arXiv)

Previous work on predicting the target of visual search from human fixations only considered closed-world settings in which training labels are available and predictions are performed for a known set of potential targets. In this work we go beyond the state of the art by studying search target prediction in an open-world setting in which we no longer assume that we have fixation data to train for the search targets. We present a dataset containing fixation data of 18 users searching for natural images from three image categories within synthesised image collages of about 80 images. In a closed-world baseline experiment we show that we can predict the correct target image out of a candidate set of five images. We then present a new problem formulation for search target prediction in the open-world setting that is based on learning compatibilities between fixations and potential targets.