[HTML][HTML] Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images
This paper presents a new multi-task neural network, called BsiNet, to delineate agricultural
fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring …
fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring …
Pixel-wise semisupervised fabric defect detection method combined with multitask mean teacher
L Shao, E Zhang, Q Ma, M Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Fabric defect detection is a crucial problem in the production of textile industry. Nowadays, a
large number of fabric defect detection algorithms based on deep learning have been …
large number of fabric defect detection algorithms based on deep learning have been …
FGAM: a pluggable light-weight attention module for medical image segmentation
Medical image segmentation is fundamental for computer-aided diagnosis or surgery.
Various attention modules are proposed to improve segmentation results, which exist some …
Various attention modules are proposed to improve segmentation results, which exist some …
[HTML][HTML] Extraction buildings from very high-resolution images with asymmetric siamese multitask networks and adversarial edge learning
X Liu, Y Li, C Dai, Z Zhang, L Ding, M Li… - International Journal of …, 2025 - Elsevier
Building extraction from very high-resolution remote-sensing images still faces two main
issues:(1) small buildings are severely omitted and the extracted building shapes have a low …
issues:(1) small buildings are severely omitted and the extracted building shapes have a low …
CCU-NET: CBAM and Cascaded Edge Detection Optimization U-NET for Remote Sensing Image Segmentation
X Cao, J Qin - … of Pioneering Computer Scientists, Engineers and …, 2024 - Springer
U-Net has been widely applied in semantic segmentation tasks, but it faces challenges in
the semantic segmentation of high-resolution remote sensing images due to the loss of …
the semantic segmentation of high-resolution remote sensing images due to the loss of …
Gray and White Matters Segmentation in Brain CT Images Using Multi-task Learning from Paired CT and MR Images
T Han, H Wang - International Workshop on New Approaches for …, 2023 - Springer
Computer tomography (CT) has been routinely used for decades in clinical neuroimaging.
Compared to Magnetic resonance imaging (MR), CT is more readily available and more cost …
Compared to Magnetic resonance imaging (MR), CT is more readily available and more cost …
[引用][C] Automated segmentaiton and classification of thyroid nodule using Cascading Convolutional Neural Networks
Y Wang, Z Xu