A Hierarchical Feature Extraction Network for Fast Scene Segmentation

Liu, Miao; Zhang, Yi

doi:10.3390/s21227730

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The authors of Ref. [110] proposed a novel scene segmentation method that enhanced the accuracy and efficiency of scene segmentation by using a hierarchical feature extraction network. The authors employed dilated convolution and multiscale feature fusion technology at different convolution layers to improve feature extraction accuracy and coverage and enable the model to capture semantic information in the image effectively.…”

Section: Pascal Voc 2012 [102]mentioning

confidence: 99%

2D Semantic Segmentation: Recent Developments and Future Directions

et al. 2023

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Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classification of the image. The research area of semantic segmentation is vast and has achieved critical advances in recent years. Deep learning architectures in particular have shown remarkable performance in generating high-level, hierarchical, and semantic features from images. Among these architectures, convolutional neural networks have been widely used to address semantic segmentation problems. This work aims to review and analyze recent technological developments in image semantic segmentation. It provides an overview of traditional and deep-learning-based approaches and analyzes their structural characteristics, strengths, and limitations. Specifically, it focuses on technical developments in deep-learning-based 2D semantic segmentation methods proposed over the past decade and discusses current challenges in semantic segmentation. The future development direction of semantic segmentation and the potential research areas that need further exploration are also examined.

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Section: Pascal Voc 2012 [102]mentioning

confidence: 99%

2D Semantic Segmentation: Recent Developments and Future Directions

et al. 2023

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A Survey on Image Semantic Segmentation Using Deep Learning Techniques

Cheng¹,

Li²,

Li³

et al. 2023

Computers, Materials &Amp; Continua

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Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation datasets are listed. Next, extensive pioneering works are deeply studied from multiple perspectives (e.g., network structures, feature fusion methods, attention mechanisms), and are divided into four categories according to different network architectures: CNN-based architectures, transformer-based architectures, MLP-based architectures, and others. Furthermore, this paper presents some common evaluation metrics and compares the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value on the most widely used datasets. Finally, possible future research directions and challenges are discussed for the reference of other researchers.

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A Hierarchical Feature Extraction Network for Fast Scene Segmentation

Cited by 2 publications

References 33 publications

2D Semantic Segmentation: Recent Developments and Future Directions

2D Semantic Segmentation: Recent Developments and Future Directions

A Survey on Image Semantic Segmentation Using Deep Learning Techniques

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