# CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research

arXiv: https://arxiv.org/abs/1505.03581 · HTML: https://mlsanity.com/paper/1505.03581/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2015.

## Built on by (most cited first, 30 of 52)

- [DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling](https://mlsanity.com/paper/2105.12441/) (2021)
- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [TranSalNet: Towards perceptually relevant visual saliency prediction](https://mlsanity.com/paper/2110.03593/) (2021)
- [MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1912.07773/) (2019)
- [GOO: A Dataset for Gaze Object Prediction in Retail Environments](https://mlsanity.com/paper/2105.10793/) (2021)
- [TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction](https://mlsanity.com/paper/2301.02315/) (2023)
- [State-of-the-Art in Human Scanpath Prediction](https://mlsanity.com/paper/2102.12239/) (2021)
- [Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency](https://mlsanity.com/paper/2105.08667/) (2021)
- [Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding](https://mlsanity.com/paper/2303.09706/) (2023)
- [Rectify ViT Shortcut Learning by Visual Saliency](https://mlsanity.com/paper/2206.08567/) (2022)
- [Beyond Average: Individualized Visual Scanpath Prediction](https://mlsanity.com/paper/2404.12235/) (2024)
- [SUM: Saliency Unification through Mamba for Visual Attention Modeling](https://mlsanity.com/paper/2406.17815/) (2024)
- [Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention](https://mlsanity.com/paper/2204.09093/) (2022)
- [UniAR: A Unified model for predicting human Attention and Responses on visual content](https://mlsanity.com/paper/2312.10175/) (2023)
- [How is Visual Attention Influenced by Text Guidance? Database and Model](https://mlsanity.com/paper/2404.07537/) (2024)
- [Brand Visibility in Packaging: A Deep Learning Approach for Logo Detection, Saliency-Map Prediction, and Logo Placement Analysis](https://mlsanity.com/paper/2403.02336/) (2024)
- [Data Augmentation via Latent Diffusion for Saliency Prediction](https://mlsanity.com/paper/2409.07307/) (2024)
- [DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural Images](https://mlsanity.com/paper/2509.16767/) (2025)
- [What Do Deep Saliency Models Learn about Visual Attention?](https://mlsanity.com/paper/2310.09679/) (2023)
- [GazeFusion: Saliency-Guided Image Generation](https://mlsanity.com/paper/2407.04191/) (2024)
- [RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation](https://mlsanity.com/paper/2410.11722/) (2024)
- [Modeling Saliency Dataset Bias](https://mlsanity.com/paper/2505.10169/) (2025)
- [A Dataset for Provident Vehicle Detection at Night](https://mlsanity.com/paper/2105.13236/) (2021)
- [Survey of Consciousness Theory from Computational Perspective](https://mlsanity.com/paper/2309.10063/) (2023)
- [Matching Representations of Explainable Artificial Intelligence and Eye Gaze for Human-Machine Interaction](https://mlsanity.com/paper/2102.00179/) (2021)
- [Modeling Object Dissimilarity for Deep Saliency Prediction](https://mlsanity.com/paper/2104.03864/) (2021)
- [Learning Saliency From Fixations](https://mlsanity.com/paper/2311.14073/) (2023)
- [DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360deg Images](https://mlsanity.com/paper/2403.17477/) (2024)
- [Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency](https://mlsanity.com/paper/2406.18588/) (2024)
- [Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images](https://mlsanity.com/paper/2506.21925/) (2025)

## Abstract (arXiv)

Saliency modeling has been an active research area in computer vision for about two decades. Existing state of the art models perform very well in predicting where people look in natural scenes. There is, however, the risk that these models may have been overfitting themselves to available small scale biased datasets, thus trapping the progress in a local minimum. To gain a deeper insight regarding current issues in saliency modeling and to better gauge progress, we recorded eye movements of 120 observers while they freely viewed a large number of naturalistic and artificial images. Our stimuli includes 4000 images; 200 from each of 20 categories covering different types of scenes such as Cartoons, Art, Objects, Low resolution images, Indoor, Outdoor, Jumbled, Random, and Line drawings. We analyze some basic properties of this dataset and compare some successful models. We believe that our dataset opens new challenges for the next generation of saliency models and helps conduct behavioral studies on bottom-up visual attention.