# Saliency-aware Stereoscopic Video Retargeting

arXiv: https://arxiv.org/abs/2304.08852 · HTML: https://mlsanity.com/paper/2304.08852/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Learning Parallax Attention for Stereo Image Super-Resolution](https://mlsanity.com/paper/1903.05784/) (2019)
- [Re-thinking Co-Salient Object Detection](https://mlsanity.com/paper/2007.03380/) (2020)
- [A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection](https://mlsanity.com/paper/2203.04708/) (2022)
- [Parallax Attention for Unsupervised Stereo Correspondence Learning](https://mlsanity.com/paper/2009.08250/) (2020)
- [Cycle-IR: Deep Cyclic Image Retargeting](https://mlsanity.com/paper/1905.03556/) (2019)
- [A New Dataset and Transformer for Stereoscopic Video Super-Resolution](https://mlsanity.com/paper/2204.10039/) (2022)
- [Fast Video Retargeting Based on Seam Carving with Parental Labeling](https://mlsanity.com/paper/1903.03180/) (2019)

## Abstract (arXiv)

Stereo video retargeting aims to resize an image to a desired aspect ratio. The quality of retargeted videos can be significantly impacted by the stereo videos spatial, temporal, and disparity coherence, all of which can be impacted by the retargeting process. Due to the lack of a publicly accessible annotated dataset, there is little research on deep learning-based methods for stereo video retargeting. This paper proposes an unsupervised deep learning-based stereo video retargeting network. Our model first detects the salient objects and shifts and warps all objects such that it minimizes the distortion of the salient parts of the stereo frames. We use 1D convolution for shifting the salient objects and design a stereo video Transformer to assist the retargeting process. To train the network, we use the parallax attention mechanism to fuse the left and right views and feed the retargeted frames to a reconstruction module that reverses the retargeted frames to the input frames. Therefore, the network is trained in an unsupervised manner. Extensive qualitative and quantitative experiments and ablation studies on KITTI stereo 2012 and 2015 datasets demonstrate the efficiency of the proposed method over the existing state-of-the-art methods. The code is available at https://github.com/z65451/SVR/.