# End-to-End Saliency Mapping via Probability Distribution Prediction

arXiv: https://arxiv.org/abs/1804.01793 · HTML: https://mlsanity.com/paper/1804.01793/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 17 of 17)

- [Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2005.14310/) (2020)
- [Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers](https://mlsanity.com/paper/2303.09383/) (2023)
- [Spatio-Temporal Self-Attention Network for Video Saliency Prediction](https://mlsanity.com/paper/2108.10696/) (2021)
- [Attention mechanisms and deep learning for machine vision: A survey of the state of the art](https://mlsanity.com/paper/2106.07550/) (2021)
- [How is Visual Attention Influenced by Text Guidance? Database and Model](https://mlsanity.com/paper/2404.07537/) (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)
- [Concept-Centric Token Interpretation for Vector-Quantized Generative Models](https://mlsanity.com/paper/2506.00698/) (2025)
- [A Gated Fusion Network for Dynamic Saliency Prediction](https://mlsanity.com/paper/2102.07682/) (2021)
- [Modeling Object Dissimilarity for Deep Saliency Prediction](https://mlsanity.com/paper/2104.03864/) (2021)
- [ImportantAug: a data augmentation agent for speech](https://mlsanity.com/paper/2112.07156/) (2021)
- [Predicting Eye Gaze Location on Websites](https://mlsanity.com/paper/2211.08074/) (2022)
- [Clustered Saliency Prediction](https://mlsanity.com/paper/2207.02205/) (2022)
- [Learning Gaussian Representation for Eye Fixation Prediction](https://mlsanity.com/paper/2403.14821/) (2024)
- [The Impact of the Single-Label Assumption in Image Recognition Benchmarking](https://mlsanity.com/paper/2412.18409/) (2024)
- [ViDaS Video Depth-aware Saliency Network](https://mlsanity.com/paper/2305.11729/) (2023)
- [ST-DiffEye: Diffusion-based Continuous Gaze Generation via Joint Scanpath-Trajectory Modeling](https://mlsanity.com/paper/2606.15486/) (2026)

## Abstract (arXiv)

Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection. Data-driven methods for training saliency models using eye-fixation data are increasingly popular, particularly with the introduction of large-scale datasets and deep architectures. However, current methods in this latter paradigm use loss functions designed for classification or regression tasks whereas saliency estimation is evaluated on topographical maps. In this work, we introduce a new saliency map model which formulates a map as a generalized Bernoulli distribution. We then train a deep architecture to predict such maps using novel loss functions which pair the softmax activation function with measures designed to compute distances between probability distributions. We show in extensive experiments the effectiveness of such loss functions over standard ones on four public benchmark datasets, and demonstrate improved performance over state-of-the-art saliency methods.