# Learning Gaze Transitions from Depth to Improve Video Saliency Estimation

arXiv: https://arxiv.org/abs/1603.03669 · HTML: https://mlsanity.com/paper/1603.03669/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2016.

## Built on by (most cited first, 9 of 9)

- [End-to-End Human-Gaze-Target Detection with Transformers](https://mlsanity.com/paper/2203.10433/) (2022)
- [Salient Object Detection in RGB-D Videos](https://mlsanity.com/paper/2310.15482/) (2023)
- [Joint Gaze-Location and Gaze-Object Detection](https://mlsanity.com/paper/2308.13857/) (2023)
- [TransGOP: Transformer-Based Gaze Object Prediction](https://mlsanity.com/paper/2402.13578/) (2024)
- [Horizontal-to-Vertical Video Conversion](https://mlsanity.com/paper/2101.04051/) (2021)
- [Clustered Saliency Prediction](https://mlsanity.com/paper/2207.02205/) (2022)
- [GaTector+: A Unified Head-free Framework for Gaze Object and Gaze Following Prediction](https://mlsanity.com/paper/2510.25301/) (2025)
- [ViDaS Video Depth-aware Saliency Network](https://mlsanity.com/paper/2305.11729/) (2023)
- [ViASNet: A Video Ad Saliency Network for Predicting Dynamic Saliency and Viewer Engagement](https://mlsanity.com/paper/2605.29302/) (2026)

## Abstract (arXiv)

In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative convolutional neural network which predicts a saliency map for a frame, given the fixation map of the previous frame. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired and yet hard to display. This can be explained, on the one hand, by the dramatic improvement of 3D-capable acquisition equipment. On the other hand, despite the considerable progress in 3D display technologies, most of the 3D displays are still expensive and require wearing special glasses. To evaluate the performance of our approach, we present a new comprehensive database of eye-fixation ground-truth for RGBD videos. Our experiments indicate that integrating depth into video saliency calculation is beneficial. We demonstrate that our approach outperforms state-of-the-art methods for video saliency, achieving 15% relative improvement.