# PET: An Eye-tracking Dataset for Animal-centric PASCAL Object Classes

arXiv: https://arxiv.org/abs/1604.01574 · HTML: https://mlsanity.com/paper/1604.01574/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2005.14310/) (2020)
- [Learning from Observer Gaze:Zero-Shot Attention Prediction Oriented by Human-Object Interaction Recognition](https://mlsanity.com/paper/2405.09931/) (2024)
- [Attention in Reasoning: Dataset, Analysis, and Modeling](https://mlsanity.com/paper/2204.09774/) (2022)
- [GazeSearch: Radiology Findings Search Benchmark](https://mlsanity.com/paper/2411.05780/) (2024)
- [Do you see what I see? An Ambiguous Optical Illusion Dataset exposing limitations of Explainable AI](https://mlsanity.com/paper/2505.21589/) (2025)

## Abstract (arXiv)

We present the Pascal animal classes Eye Tracking database. Our database comprises eye movement recordings compiled from forty users for the bird, cat, cow, dog, horse and sheep trainval sets from the VOC 2012 image set. Different from recent eye-tracking databases such as citekiwoncvpr13gaze,PapadopoulosCKF14, a salient aspect of PET is that it contains eye movements recorded for both the free-viewing and visual search task conditions. While some differences in terms of overall gaze behavior and scanning patterns are observed between the two conditions, a very similar number of fixations are observed on target objects for both conditions. As a utility application, we show how feature pooling around fixated locations enables enhanced (animal) object classification accuracy.