# Guided Upsampling Network for Real-Time Semantic Segmentation

arXiv: https://arxiv.org/abs/1807.07466 · HTML: https://mlsanity.com/paper/1807.07466/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 24 of 24)

- [FeatUp: A Model-Agnostic Framework for Features at Any Resolution](https://mlsanity.com/paper/2403.10516/) (2024)
- [FaPN: Feature-aligned Pyramid Network for Dense Image Prediction](https://mlsanity.com/paper/2108.07058/) (2021)
- [Frequency-aware Feature Fusion for Dense Image Prediction](https://mlsanity.com/paper/2408.12879/) (2024)
- [FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling](https://mlsanity.com/paper/2207.10392/) (2022)
- [Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation](https://mlsanity.com/paper/2203.15227/) (2022)
- [Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes](https://mlsanity.com/paper/2203.04031/) (2022)
- [When Semantic Segmentation Meets Frequency Aliasing](https://mlsanity.com/paper/2403.09065/) (2024)
- [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)
- [A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling](https://mlsanity.com/paper/2407.02283/) (2024)
- [Representation Separation for Semantic Segmentation with Vision Transformers](https://mlsanity.com/paper/2212.13764/) (2022)
- [Probability-based Global Cross-modal Upsampling for Pansharpening](https://mlsanity.com/paper/2303.13659/) (2023)
- [Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement](https://mlsanity.com/paper/2109.02580/) (2021)
- [Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature Refinement](https://mlsanity.com/paper/2301.03832/) (2023)
- [Guided Linear Upsampling](https://mlsanity.com/paper/2307.09582/) (2023)
- [FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures](https://mlsanity.com/paper/2407.13500/) (2024)
- [A Deep Semantic Segmentation Network with Semantic and Contextual Refinements](https://mlsanity.com/paper/2412.08671/) (2024)
- [Spatial Frequency Modulation for Semantic Segmentation](https://mlsanity.com/paper/2507.11893/) (2025)
- [Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks](https://mlsanity.com/paper/2103.03435/) (2021)
- [CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/2106.04400/) (2021)
- [CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation](https://mlsanity.com/paper/2210.05318/) (2022)
- [P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation](https://mlsanity.com/paper/2310.15025/) (2023)
- [Attention-Mamba: A Mamba-Enhanced Multi-Scale Parallel Inference Network for Medical Image Segmentation](https://mlsanity.com/paper/2402.02286/) (2024)
- [Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels](https://mlsanity.com/paper/2410.22139/) (2024)

## Abstract (arXiv)

Semantic segmentation architectures are mainly built upon an encoder-decoder structure. These models perform subsequent downsampling operations in the encoder. Since operations on high-resolution activation maps are computationally expensive, usually the decoder produces output segmentation maps by upsampling with parameters-free operators like bilinear or nearest-neighbor. We propose a Neural Network named Guided Upsampling Network which consists of a multiresolution architecture that jointly exploits high-resolution and large context information. Then we introduce a new module named Guided Upsampling Module (GUM) that enriches upsampling operators by introducing a learnable transformation for semantic maps. It can be plugged into any existing encoder-decoder architecture with little modifications and low additional computation cost. We show with quantitative and qualitative experiments how our network benefits from the use of GUM module. A comprehensive set of experiments on the publicly available Cityscapes dataset demonstrates that Guided Upsampling Network can efficiently process high-resolution images in real-time while attaining state-of-the art performances.