Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task
The semantic segmentation (SS) task aims to create a dense classification by labeling at the pixel level each object present on images. Convolutional neural network (CNN) approaches have been widely used, and exhibited the best results in this task. However, the loss of spatial precision on the resu...
Main Authors: | Darwin Saire, Adin Ramirez Rivera |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9444361/ |
Similar Items
-
Global and Local Features Through Gaussian Mixture Models on Image Semantic Segmentation
by: Darwin Saire, et al.
Published: (2022-01-01) -
Multi-task Learning of Semantic Segmentation and Height Estimation for Multi-modal Remote Sensing Images
by: Mengyu WANG, Zhiyuan YAN, Yingchao FENG, Wenhui DIAO, Xian SUN
Published: (2023-12-01) -
Multi-Task CNN Model for Attribute Prediction
by: Abdulnabi, Abrar H., et al.
Published: (2016) -
MQANet: Multi-Task Quadruple Attention Network of Multi-Object Semantic Segmentation from Remote Sensing Images
by: Yuxia Li, et al.
Published: (2022-12-01) -
Interactive Efficient Multi-Task Network for RGB-D Semantic Segmentation
by: Xinhua Xu, et al.
Published: (2023-09-01)