# Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation

arXiv: https://arxiv.org/abs/2405.06228 · HTML: https://mlsanity.com/paper/2405.06228/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 44 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 30 of 38)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://mlsanity.com/paper/1904.11492/) (2019)
- [Context Encoding for Semantic Segmentation](https://mlsanity.com/paper/1803.08904/) (2018)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Expectation-Maximization Attention Networks for Semantic Segmentation](https://mlsanity.com/paper/1907.13426/) (2019)
- [Rethinking Semantic Segmentation: A Prototype View](https://mlsanity.com/paper/2203.15102/) (2022)
- [Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks](https://mlsanity.com/paper/1810.12348/) (2018)
- [Rethinking BiSeNet For Real-time Semantic Segmentation](https://mlsanity.com/paper/2104.13188/) (2021)
- [Rethinking Vision Transformers for MobileNet Size and Speed](https://mlsanity.com/paper/2212.08059/) (2022)
- [SegViT: Semantic Segmentation with Plain Vision Transformers](https://mlsanity.com/paper/2210.05844/) (2022)
- [DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1904.02216/) (2019)

## Built on by (most cited first, 25 of 25)

- [Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos](https://mlsanity.com/paper/2501.04001/) (2025)
- [SegMAN: Omni-scale Context Modeling with State Space Models and Local Attention for Semantic Segmentation](https://mlsanity.com/paper/2412.11890/) (2024)
- [Deformable Mamba for Wide Field of View Segmentation](https://mlsanity.com/paper/2411.16481/) (2024)
- [Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment](https://mlsanity.com/paper/2508.08811/) (2025)
- [Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion](https://mlsanity.com/paper/2602.01183/) (2026)
- [MambaSeg: Harnessing Mamba for Accurate and Efficient Image-Event Semantic Segmentation](https://mlsanity.com/paper/2512.24243/) (2025)
- [Diffusion Model as a Generalist Segmentation Learner](https://mlsanity.com/paper/2604.24575/) (2026)
- [FMLGS: Fast Multilevel Language Embedded Gaussians for Part-level Interactive Agents](https://mlsanity.com/paper/2504.08581/) (2025)
- [MaSS13K: A Matting-level Semantic Segmentation Benchmark](https://mlsanity.com/paper/2503.18364/) (2025)
- [Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification](https://mlsanity.com/paper/2507.21703/) (2025)
- [HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone](https://mlsanity.com/paper/2605.17286/) (2026)
- [SLTNet: Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks](https://mlsanity.com/paper/2412.12843/) (2024)
- [ContextFormer: Redefining Efficiency in Semantic Segmentation](https://mlsanity.com/paper/2501.19255/) (2025)
- [A2Mamba: Attention-augmented State Space Models for Visual Recognition](https://mlsanity.com/paper/2507.16624/) (2025)
- [Segment Any Vehicle: Semantic and Visual Context Driven SAM and A Benchmark](https://mlsanity.com/paper/2508.04260/) (2025)
- [Restoration Adaptation for Semantic Segmentation on Low Quality Images](https://mlsanity.com/paper/2602.14042/) (2026)
- [UAV-DETR: DETR for Anti-Drone Target Detection](https://mlsanity.com/paper/2603.22841/) (2026)
- [Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection](https://mlsanity.com/paper/2501.12104/) (2025)
- [Masked Representation Modeling for Domain-Adaptive Segmentation](https://mlsanity.com/paper/2509.13801/) (2025)
- [Intelligent Communication Mixture-of-Experts Boosted-Medical Image Segmentation Foundation Model](https://mlsanity.com/paper/2510.17684/) (2025)
- [WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition](https://mlsanity.com/paper/2510.21079/) (2025)
- [Chatting about Upper-Body Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2604.17959/) (2026)
- [Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement](https://mlsanity.com/paper/2607.13925/) (2026)
- [DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding](https://mlsanity.com/paper/2607.23070/) (2026)
- [CoordFormer: Give Me Any Coordinates and I Will Give You Labels](https://mlsanity.com/paper/2609.08660/) (2026)

## Abstract (arXiv)

Semantic segmentation is an important task for numerous applications but it is still quite challenging to achieve advanced performance with limited computational costs. In this paper, we present CGRSeg, an efficient yet competitive segmentation framework based on context-guided spatial feature reconstruction. A Rectangular Self-Calibration Module is carefully designed for spatial feature reconstruction and pyramid context extraction. It captures the axial global context in both horizontal and vertical directions to explicitly model rectangular key areas. A shape self-calibration function is designed to make the key areas closer to foreground objects. Besides, a lightweight Dynamic Prototype Guided head is proposed to improve the classification of foreground objects by explicit class embedding. Our CGRSeg is extensively evaluated on ADE20K, COCO-Stuff, and Pascal Context benchmarks, and achieves state-of-the-art semantic performance. Specifically, it achieves 43.6% mIoU on ADE20K with only 4.0 GFLOPs, which is 0.9% and 2.5% mIoU better than SeaFormer and SegNeXt but with about 38.0% fewer GFLOPs. Code is available at https://github.com/nizhenliang/CGRSeg.