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Related Experiment Video

Updated: Feb 6, 2026

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

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ECMNet: Lightweight semantic segmentation with efficient CNN-Mamba network.

Feixiang Du^1,2, Shengkun Wu¹, Xiang Wang¹

¹School of Electrical Engineering, Tongling University, Tongling, Anhui, China.

Science Progress

|February 4, 2026

Summary

This summary is machine-generated.

This study introduces ECMNet, a novel lightweight CNN-Mamba network for semantic segmentation. ECMNet enhances global context modeling and achieves a superior balance between accuracy and efficiency in vision tasks.

Keywords:

Mamba Semantic segmentation convolutional neural network feature fusion lightweight

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Convolutional Neural Networks (CNNs) and transformers are widely used in semantic segmentation but struggle with global context modeling.
Mamba shows promise in vision tasks for modeling long-range dependencies, addressing limitations of existing models.

Purpose of the Study:

To propose a lightweight and efficient CNN-Mamba network (ECMNet) for semantic segmentation.
To combine the strengths of CNNs and Mamba to overcome their respective weaknesses in feature representation and context modeling.

Main Methods:

Developed ECMNet, a capsule-based framework integrating CNNs and Mamba.
Designed an enhanced dual-attention block for lightweight bottlenecks.
Devised a multi-scale attention unit for feature aggregation (multi-scale, spatial, channel).
Implemented a Mamba-enhanced feature fusion module for improved segmentation accuracy.

Main Results:

ECMNet achieves a balance of accuracy and efficiency.
Attained 70.6% mIoU on Cityscapes and 73.6% mIoU on CamVid test datasets.
The model has 0.87M parameters and 8.27G FLOPs, demonstrating efficiency.

Conclusions:

ECMNet effectively models long-range dependencies and enhances feature representation for semantic segmentation.
The proposed network offers a competitive and efficient solution for semantic segmentation tasks.
Source code is available for further research and application.