# Real-Time Semantic Segmentation via Multiply Spatial Fusion Network

arXiv: https://arxiv.org/abs/1911.07217 · HTML: https://mlsanity.com/paper/1911.07217/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022)
- [Rethinking Dilated Convolution for Real-time Semantic Segmentation](https://mlsanity.com/paper/2111.09957/) (2021)
- [SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow](https://mlsanity.com/paper/2207.04415/) (2022)
- [SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision](https://mlsanity.com/paper/2207.06242/) (2022)
- [Lightweight and Progressively-Scalable Networks for Semantic Segmentation](https://mlsanity.com/paper/2207.13600/) (2022)
- [A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design](https://mlsanity.com/paper/2303.04315/) (2023)
- [Reparameterizable Dual-Resolution Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/2406.12496/) (2024)
- [Feature Reuse and Fusion for Real-time Semantic segmentation](https://mlsanity.com/paper/2105.12964/) (2021)
- [Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/2203.00436/) (2022)
- [Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation](https://mlsanity.com/paper/2206.09604/) (2022)
- [MFPNet: Multi-scale Feature Propagation Network For Lightweight Semantic Segmentation](https://mlsanity.com/paper/2309.04914/) (2023)
- [Spatial-Assistant Encoder-Decoder Network for Real Time Semantic Segmentation](https://mlsanity.com/paper/2309.10519/) (2023)
- [ICFRNet: Image Complexity Prior Guided Feature Refinement for Real-time Semantic Segmentation](https://mlsanity.com/paper/2408.13771/) (2024)

## Abstract (arXiv)

Real-time semantic segmentation plays a significant role in industry applications, such as autonomous driving, robotics and so on. It is a challenging task as both efficiency and performance need to be considered simultaneously. To address such a complex task, this paper proposes an efficient CNN called Multiply Spatial Fusion Network (MSFNet) to achieve fast and accurate perception. The proposed MSFNet uses Class Boundary Supervision to process the relevant boundary information based on our proposed Multi-features Fusion Module which can obtain spatial information and enlarge receptive field. Therefore, the final upsampling of the feature maps of 1/8 original image size can achieve impressive results while maintaining a high speed. Experiments on Cityscapes and Camvid datasets show an obvious advantage of the proposed approach compared with the existing approaches. Specifically, it achieves 77.1% Mean IOU on the Cityscapes test dataset with the speed of 41 FPS for a 1024*2048 input, and 75.4% Mean IOU with the speed of 91 FPS on the Camvid test dataset.