# Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation

arXiv: https://arxiv.org/abs/2005.11034 · HTML: https://mlsanity.com/paper/2005.11034/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 7 of 7)

- [Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN](https://mlsanity.com/paper/2302.10484/) (2023)
- [SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks](https://mlsanity.com/paper/2401.15741/) (2024)
- [HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation](https://mlsanity.com/paper/2407.07441/) (2024)
- [A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design](https://mlsanity.com/paper/2303.04315/) (2023)
- [Efficient Semantic Segmentation via Lightweight Multiple-Information Interaction Network](https://mlsanity.com/paper/2410.02224/) (2024)
- [MUSTER: A Multi-scale Transformer-based Decoder for Semantic Segmentation](https://mlsanity.com/paper/2211.13928/) (2022)
- [MFPNet: Multi-scale Feature Propagation Network For Lightweight Semantic Segmentation](https://mlsanity.com/paper/2309.04914/) (2023)

## Abstract (arXiv)

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Citysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768x1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters.