# Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction

arXiv: https://arxiv.org/abs/2010.01220 · HTML: https://mlsanity.com/paper/2010.01220/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 24 of 24)

- [In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation](https://mlsanity.com/paper/2208.04464/) (2022)
- [Temporal-Spatial Feature Pyramid for Video Saliency Detection](https://mlsanity.com/paper/2105.04213/) (2021)
- [Spatio-Temporal Self-Attention Network for Video Saliency Prediction](https://mlsanity.com/paper/2108.10696/) (2021)
- [Domain-Specific Bias Filtering for Single Labeled Domain Generalization](https://mlsanity.com/paper/2110.00726/) (2021)
- [SalFoM: Dynamic Saliency Prediction with Video Foundation Models](https://mlsanity.com/paper/2404.03097/) (2024)
- [A Comprehensive Survey on Video Saliency Detection with Auditory Information: the Audio-visual Consistency Perceptual is the Key!](https://mlsanity.com/paper/2206.13390/) (2022)
- [TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation](https://mlsanity.com/paper/2301.04619/) (2023)
- [Transformer-based Video Saliency Prediction with High Temporal Dimension Decoding](https://mlsanity.com/paper/2401.07942/) (2024)
- [Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasets](https://mlsanity.com/paper/2403.14534/) (2024)
- [From Sight to Insight: Unleashing Eye-Tracking in Weakly Supervised Video Salient Object Detection](https://mlsanity.com/paper/2506.23519/) (2025)
- [Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization](https://mlsanity.com/paper/2503.13617/) (2025)
- [Joint Learning of Visual-Audio Saliency Prediction and Sound Source Localization on Multi-face Videos](https://mlsanity.com/paper/2111.08567/) (2021)
- [Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception](https://mlsanity.com/paper/2112.13697/) (2021)
- [NPF-200: A Multi-Modal Eye Fixation Dataset and Method for Non-Photorealistic Videos](https://mlsanity.com/paper/2308.12163/) (2023)
- [Evaluating the Significance of Outdoor Advertising from Driver's Perspective Using Computer Vision](https://mlsanity.com/paper/2311.07390/) (2023)
- [Selective Attention-based Modulation for Continual Learning](https://mlsanity.com/paper/2403.20086/) (2024)
- [Unified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models](https://mlsanity.com/paper/2405.02929/) (2024)
- [Relevance-guided Audio Visual Fusion for Video Saliency Prediction](https://mlsanity.com/paper/2411.11454/) (2024)
- [DTFSal: Audio-Visual Dynamic Token Fusion for Video Saliency Prediction](https://mlsanity.com/paper/2504.10070/) (2025)
- [Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model](https://mlsanity.com/paper/2605.23790/) (2026)
- [Attend to Anything: Foundation Model for Unified Human Attention Modeling](https://mlsanity.com/paper/2606.03540/) (2026)
- [Saliency Detection in Educational Videos: Analyzing the Performance of Current Models, Identifying Limitations and Advancement Directions](https://mlsanity.com/paper/2408.04515/) (2024)
- [Saliency-guided Emotion Modeling: Predicting Viewer Reactions from Video Stimuli](https://mlsanity.com/paper/2505.19178/) (2025)
- [BIAS: A Biologically Inspired Algorithm for Video Saliency Detection](https://mlsanity.com/paper/2604.08858/) (2026)

## Abstract (arXiv)

In this work, we propose a 3D fully convolutional architecture for video saliency prediction that employs hierarchical supervision on intermediate maps (referred to as conspicuity maps) generated using features extracted at different abstraction levels. We provide the base hierarchical learning mechanism with two techniques for domain adaptation and domain-specific learning. For the former, we encourage the model to unsupervisedly learn hierarchical general features using gradient reversal at multiple scales, to enhance generalization capabilities on datasets for which no annotations are provided during training. As for domain specialization, we employ domain-specific operations (namely, priors, smoothing and batch normalization) by specializing the learned features on individual datasets in order to maximize performance. The results of our experiments show that the proposed model yields state-of-the-art accuracy on supervised saliency prediction. When the base hierarchical model is empowered with domain-specific modules, performance improves, outperforming state-of-the-art models on three out of five metrics on the DHF1K benchmark and reaching the second-best results on the other two. When, instead, we test it in an unsupervised domain adaptation setting, by enabling hierarchical gradient reversal layers, we obtain performance comparable to supervised state-of-the-art.