# ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time

arXiv: https://arxiv.org/abs/1805.04554 · HTML: https://mlsanity.com/paper/1805.04554/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 30 of 31)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.08623/) (2021)
- [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022)
- [Enhanced Boundary Learning for Glass-like Object Segmentation](https://mlsanity.com/paper/2103.15734/) (2021)
- [Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World](https://mlsanity.com/paper/2107.03172/) (2021)
- [Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection](https://mlsanity.com/paper/2206.00515/) (2022)
- [Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark](https://mlsanity.com/paper/2202.07054/) (2022)
- [Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance](https://mlsanity.com/paper/2108.09174/) (2021)
- [TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation](https://mlsanity.com/paper/2202.13393/) (2022)
- [CFPNet: Channel-wise Feature Pyramid for Real-Time Semantic Segmentation](https://mlsanity.com/paper/2103.12212/) (2021)
- [MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation](https://mlsanity.com/paper/2103.13044/) (2021)
- [Computation-efficient Deep Learning for Computer Vision: A Survey](https://mlsanity.com/paper/2308.13998/) (2023)
- [Segmenting Transparent Object in the Wild with Transformer](https://mlsanity.com/paper/2101.08461/) (2021)
- [Highlighting the Importance of Reducing Research Bias and Carbon Emissions in CNNs](https://mlsanity.com/paper/2106.03242/) (2021)
- [PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a Large-Scale Teleconferencing Video Dataset](https://mlsanity.com/paper/2112.07146/) (2021)
- [On Efficient Real-Time Semantic Segmentation: A Survey](https://mlsanity.com/paper/2206.08605/) (2022)
- [Backdoor Attacks for Remote Sensing Data with Wavelet Transform](https://mlsanity.com/paper/2211.08044/) (2022)
- [Exploiting Low-level Representations for Ultra-Fast Road Segmentation](https://mlsanity.com/paper/2402.02430/) (2024)
- [Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement](https://mlsanity.com/paper/2109.02580/) (2021)
- [Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI](https://mlsanity.com/paper/2205.01675/) (2022)
- [Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model](https://mlsanity.com/paper/2402.13122/) (2024)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [EFCNet: Every Feature Counts for Small Medical Object Segmentation](https://mlsanity.com/paper/2406.18201/) (2024)
- [GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation](https://mlsanity.com/paper/2505.01057/) (2025)
- [A feature refinement module for light-weight semantic segmentation network](https://mlsanity.com/paper/2412.08670/) (2024)
- [Efficient Context Integration through Factorized Pyramidal Learning for Ultra-Lightweight Semantic Segmentation](https://mlsanity.com/paper/2302.11785/) (2023)
- [P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation](https://mlsanity.com/paper/2310.15025/) (2023)
- [MCFNet: Multi-scale Covariance Feature Fusion Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/2312.07207/) (2023)
- [BAFNet: Bilateral Attention Fusion Network for Lightweight Semantic Segmentation of Urban Remote Sensing Images](https://mlsanity.com/paper/2409.10269/) (2024)

## Abstract (arXiv)

Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transferable to real-time applications or embedded devices, since naive adaptation of such systems to reduce computational cost (speed, memory and energy) causes a significant drop in accuracy. We propose ContextNet, a new deep neural network architecture which builds on factorized convolution, network compression and pyramid representation to produce competitive semantic segmentation in real-time with low memory requirement. ContextNet combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on high-resolution segmentation details. We analyse our network in a thorough ablation study and present results on the Cityscapes dataset, achieving 66.1% accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps with pipelined computations for streamed data).