Introducing Semantic-Based Receptive Field into Semantic Segmentation via Graph Neural Networks

Daixi Jia; Hang Gao; Xingzhe Su; Fengge Wu; Junsuo Zhao

doi:10.1007/978-981-99-8076-5_32

ScienceGate Book Chapters

BOOK-CHAPTER

Introducing Semantic-Based Receptive Field into Semantic Segmentation via Graph Neural Networks

Daixi Jia Hang Gao Xingzhe Su Fengge Wu Junsuo Zhao

Year: 2023 Lecture notes in computer science Pages: 434-451 Publisher: Springer Science+Business Media

DOI: 10.1007/978-981-99-8076-5_32

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Segmentation Artificial intelligence Pattern recognition (psychology) Robustness (evolution) Graph Interpretability Convolutional neural network Machine learning Theoretical computer science

Metrics

Cited By

1.07

FWCI (Field Weighted Citation Impact)

Refs

0.81

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

Advanced Graph Neural Networks

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning in Materials Science

Physical Sciences → Materials Science → Materials Chemistry

Introducing Semantic-Based Receptive Field into Semantic Segmentation via Graph Neural Networks

Metrics

Citation History

Topics

Related Documents

Enlarging Effective Receptive Field of Convolutional Neural Networks for Better Semantic Segmentation

3D Graph Neural Networks for RGBD Semantic Segmentation

Graph-boosted convolutional neural networks for semantic segmentation

SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

Multi Receptive Field Network for Semantic Segmentation