Lightweight multi-scale attention-guided network for real-time semantic segmentation

Xuegang Hu; Yuanjing Liu

doi:10.1016/j.imavis.2023.104823

ScienceGate Book Chapters

JOURNAL ARTICLE

Lightweight multi-scale attention-guided network for real-time semantic segmentation

Xuegang Hu Yuanjing Liu

Year: 2023 Journal: Image and Vision Computing Vol: 139 Pages: 104823-104823 Publisher: Elsevier BV

DOI: 10.1016/j.imavis.2023.104823

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Segmentation Encoder Feature (linguistics) Inference Context (archaeology) Pyramid (geometry) Artificial intelligence Scalability Semantic feature Data mining Pattern recognition (psychology) Machine learning

Metrics

Cited By

2.55

FWCI (Field Weighted Citation Impact)

Refs

0.88

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Neural Network Applications

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Infrastructure Maintenance and Monitoring

Physical Sciences → Engineering → Civil and Structural Engineering

Video Surveillance and Tracking Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Lightweight multi-scale attention-guided network for real-time semantic segmentation

Metrics

Citation History

Topics

Related Documents

LAGNet: Lightweight Attention Guided Network for Real-time Semantic Segmentation

LAANet: lightweight attention-guided asymmetric network for real-time semantic segmentation

ELANet: Effective Lightweight Attention-Guided Network for Real-Time Semantic Segmentation

Lightweight attention‐guided redundancy‐reuse network for real‐time semantic segmentation

GLIMS: Attention-guided lightweight multi-scale hybrid network for volumetric semantic segmentation