# Contextual Encoder-Decoder Network for Visual Saliency Prediction

arXiv: https://arxiv.org/abs/1902.06634 · HTML: https://mlsanity.com/paper/1902.06634/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 30 of 44)

- [DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling](https://mlsanity.com/paper/2105.12441/) (2021)
- [In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation](https://mlsanity.com/paper/2208.04464/) (2022)
- [TranSalNet: Towards perceptually relevant visual saliency prediction](https://mlsanity.com/paper/2110.03593/) (2021)
- [YouRefIt: Embodied Reference Understanding with Language and Gesture](https://mlsanity.com/paper/2109.03413/) (2021)
- [TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction](https://mlsanity.com/paper/2301.02315/) (2023)
- [Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency](https://mlsanity.com/paper/2105.08667/) (2021)
- [Spatio-Temporal Self-Attention Network for Video Saliency Prediction](https://mlsanity.com/paper/2108.10696/) (2021)
- [SUM: Saliency Unification through Mamba for Visual Attention Modeling](https://mlsanity.com/paper/2406.17815/) (2024)
- [Spatial and Visual Perspective-Taking via View Rotation and Relation Reasoning for Embodied Reference Understanding](https://mlsanity.com/paper/2309.01073/) (2023)
- [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2604.14816/) (2026)
- [Instance-Level Relative Saliency Ranking with Graph Reasoning](https://mlsanity.com/paper/2107.03824/) (2021)
- [SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks](https://mlsanity.com/paper/2112.03731/) (2021)
- [AIM 2024 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2409.14827/) (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)
- [Learning to Highlight Audio by Watching Movies](https://mlsanity.com/paper/2505.12154/) (2025)
- [Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images](https://mlsanity.com/paper/2202.07118/) (2022)
- [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)
- [Where to look at the movies : Analyzing visual attention to understand movie editing](https://mlsanity.com/paper/2102.13378/) (2021)
- [Modeling Object Dissimilarity for Deep Saliency Prediction](https://mlsanity.com/paper/2104.03864/) (2021)
- [Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction](https://mlsanity.com/paper/2109.08371/) (2021)
- [Fast Hybrid Image Retargeting](https://mlsanity.com/paper/2203.13595/) (2022)
- [Context-empowered Visual Attention Prediction in Pedestrian Scenarios](https://mlsanity.com/paper/2210.16933/) (2022)
- [Predicting Eye Gaze Location on Websites](https://mlsanity.com/paper/2211.08074/) (2022)
- [SR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate Reduction](https://mlsanity.com/paper/2305.04844/) (2023)
- [Learning Saliency From Fixations](https://mlsanity.com/paper/2311.14073/) (2023)
- [DeepRare: Generic Unsupervised Visual Attention Models](https://mlsanity.com/paper/2109.11439/) (2021)
- [Continuous Levels of Detail for Light Field Networks](https://mlsanity.com/paper/2309.11591/) (2023)
- [Bridging the Gap Between Saliency Prediction and Image Quality Assessment](https://mlsanity.com/paper/2405.04997/) (2024)
- [SalNAS: Efficient Saliency-prediction Neural Architecture Search with self-knowledge distillation](https://mlsanity.com/paper/2407.20062/) (2024)

## Abstract (arXiv)

Predicting salient regions in natural images requires the detection of objects that are present in a scene. To develop robust representations for this challenging task, high-level visual features at multiple spatial scales must be extracted and augmented with contextual information. However, existing models aimed at explaining human fixation maps do not incorporate such a mechanism explicitly. Here we propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. The architecture forms an encoder-decoder structure and includes a module with multiple convolutional layers at different dilation rates to capture multi-scale features in parallel. Moreover, we combine the resulting representations with global scene information for accurately predicting visual saliency. Our model achieves competitive and consistent results across multiple evaluation metrics on two public saliency benchmarks and we demonstrate the effectiveness of the suggested approach on five datasets and selected examples. Compared to state of the art approaches, the network is based on a lightweight image classification backbone and hence presents a suitable choice for applications with limited computational resources, such as (virtual) robotic systems, to estimate human fixations across complex natural scenes.