Review the state-of-the-art technologies of semantic segmentation based on deep learning
The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …
information and predict the semantic category of each pixel from a given label set. With the …
A brief survey on semantic segmentation with deep learning
S Hao, Y Zhou, Y Guo - Neurocomputing, 2020 - Elsevier
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
An effective CNN and Transformer complementary network for medical image segmentation
F Yuan, Z Zhang, Z Fang - Pattern Recognition, 2023 - Elsevier
The Transformer network was originally proposed for natural language processing. Due to
its powerful representation ability for long-range dependency, it has been extended for …
its powerful representation ability for long-range dependency, it has been extended for …
PIDNet: A real-time semantic segmentation network inspired by PID controllers
Two-branch network architecture has shown its efficiency and effectiveness in real-time
semantic segmentation tasks. However, direct fusion of high-resolution details and low …
semantic segmentation tasks. However, direct fusion of high-resolution details and low …
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …
of practical applications, such as land cover mapping, urban change detection …
Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation
Low-level details and high-level semantics are both essential to the semantic segmentation
task. However, to speed up the model inference, current approaches almost always sacrifice …
task. However, to speed up the model inference, current approaches almost always sacrifice …
Fast-scnn: Fast semantic segmentation network
The encoder-decoder framework is state-of-the-art for offline semantic image segmentation.
Since the rise in autonomous systems, real-time computation is increasingly desirable. In …
Since the rise in autonomous systems, real-time computation is increasingly desirable. In …
Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network
We introduce a light-weight, power efficient, and general purpose convolutional neural
network, ESPNetv2, for modeling visual and sequential data. Our network uses group point …
network, ESPNetv2, for modeling visual and sequential data. Our network uses group point …
Levit-unet: Make faster encoders with transformer for medical image segmentation
Medical image segmentation plays an essential role in developing computer-assisted
diagnosis and treatment systems, yet it still faces numerous challenges. In the past few …
diagnosis and treatment systems, yet it still faces numerous challenges. In the past few …
Landslide4sense: Reference benchmark data and deep learning models for landslide detection
This study introduces\textit {Landslide4Sense}, a reference benchmark for landslide
detection from remote sensing. The repository features 3,799 image patches fusing optical …
detection from remote sensing. The repository features 3,799 image patches fusing optical …