# Stereo Superpixel Segmentation Via Decoupled Dynamic Spatial-Embedding Fusion Network

arXiv: https://arxiv.org/abs/2208.08145 · HTML: https://mlsanity.com/paper/2208.08145/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Siamese Box Adaptive Network for Visual Tracking](https://mlsanity.com/paper/2003.06761/) (2020)
- [Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features](https://mlsanity.com/paper/1806.04659/) (2018)
- [Superpixel Sampling Networks](https://mlsanity.com/paper/1807.10174/) (2018)
- [STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/1509.03150/) (2015)
- [Superpixel Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2003.12929/) (2020)
- [Learning Parallax Attention for Stereo Image Super-Resolution](https://mlsanity.com/paper/1903.05784/) (2019)
- [Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation](https://mlsanity.com/paper/1710.05172/) (2017)
- [Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning](https://mlsanity.com/paper/1703.01290/) (2017)

## Built on by (most cited first, 2 of 2)

- [Modality-Collaborative Low-Rank Decomposers for Few-Shot Video Domain Adaptation](https://mlsanity.com/paper/2511.18711/) (2025)
- [A Cosine Network for Image Super-Resolution](https://mlsanity.com/paper/2601.16413/) (2026)

## Abstract (arXiv)

Stereo superpixel segmentation aims at grouping the discretizing pixels into perceptual regions through left and right views more collaboratively and efficiently. Existing superpixel segmentation algorithms mostly utilize color and spatial features as input, which may impose strong constraints on spatial information while utilizing the disparity information in terms of stereo image pairs. To alleviate this issue, we propose a stereo superpixel segmentation method with a decoupling mechanism of spatial information in this work. To decouple stereo disparity information and spatial information, the spatial information is temporarily removed before fusing the features of stereo image pairs, and a decoupled stereo fusion module (DSFM) is proposed to handle the stereo features alignment as well as occlusion problems. Moreover, since the spatial information is vital to superpixel segmentation, we further design a dynamic spatiality embedding module (DSEM) to re-add spatial information, and the weights of spatial information will be adaptively adjusted through the dynamic fusion (DF) mechanism in DSEM for achieving a finer segmentation. Comprehensive experimental results demonstrate that our method can achieve the state-of-the-art performance on the KITTI2015 and Cityscapes datasets, and also verify the efficiency when applied in salient object detection on NJU2K dataset. The source code will be available publicly after paper is accepted.