Open Access

ARTICLE

CGMISeg: Context-Guided Multi-Scale Interactive for Efficient Semantic Segmentation

Ze Wang, Jin Qin, Chuhua Huang^*, Yongjun Zhang^*

The State Key Laboratory of Public Big Data and College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

* Corresponding Authors: Chuhua Huang. Email: ; Yongjun Zhang. Email:

(This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)

Computers, Materials & Continua 2025, 84(3), 5811-5829. https://doi.org/10.32604/cmc.2025.064537

Received 18 February 2025; Accepted 19 May 2025; Issue published 30 July 2025

Abstract

Semantic segmentation has made significant breakthroughs in various application fields, but achieving both accurate and efficient segmentation with limited computational resources remains a major challenge. To this end, we propose CGMISeg, an efficient semantic segmentation architecture based on a context-guided multi-scale interaction strategy, aiming to significantly reduce computational overhead while maintaining segmentation accuracy. CGMISeg consists of three core components: context-aware attention modulation, feature reconstruction, and cross-information fusion. Context-aware attention modulation is carefully designed to capture key contextual information through channel and spatial attention mechanisms. The feature reconstruction module reconstructs contextual information from different scales, modeling key rectangular areas by capturing critical contextual information in both horizontal and vertical directions, thereby enhancing the focus on foreground features. The cross-information fusion module aims to fuse the reconstructed high-level features with the original low-level features during upsampling, promoting multi-scale interaction and enhancing the model’s ability to handle objects at different scales. We extensively evaluated CGMISeg on ADE20K, Cityscapes, and COCO-Stuff, three widely used datasets benchmarks, and the experimental results show that CGMISeg exhibits significant advantages in segmentation performance, computational efficiency, and inference speed, clearly outperforming several mainstream methods, including SegFormer, Feedformer, and SegNext. Specifically, CGMISeg achieves 42.9% mIoU (Mean Intersection over Union) and 15.7 FPS (Frames Per Second) on the ADE20K dataset with 3.8 GFLOPs (Giga Floating-point Operations Per Second), outperforming Feedformer and SegNeXt by 3.7% and 1.8% in mIoU, respectively, while also offering reduced computational complexity and faster inference. CGMISeg strikes an excellent balance between accuracy and efficiency, significantly enhancing both computational and inference performance while maintaining high precision, showcasing exceptional practical value and strong potential for widespread applications.

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