Meta-Seg: A Generalized Meta-Learning Framework for Multi-Class Few-Shot Semantic Segmentation
Semantic segmentation performs pixel-wise classification for given images, which can be widely used in autonomous driving, robotics, medical diagnostics and etc. The recent advanced approaches have witnessed rapid progress in semantic segmentation. However, these supervised learning based methods re...
Saved in:
Main Authors: | Zhiying Cao, Tengfei Zhang, Wenhui Diao, Yue Zhang, Xiaode Lyu, Kun Fu, Xian Sun |
---|---|
Format: | article |
Language: | EN |
Published: |
IEEE
2019
|
Subjects: | |
Online Access: | https://doaj.org/article/c733ea5763c84041ba4ff1446090b10c |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Few-Shot Object Detection via Sample Processing
by: Honghui Xu, et al.
Published: (2021) -
Task-Adaptive Embedding Learning with Dynamic Kernel Fusion for Few-Shot Remote Sensing Scene Classification
by: Pei Zhang, et al.
Published: (2021) -
Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle
by: Ali Shojaeipour, et al.
Published: (2021) -
Optimizing Few-Shot Learning Based on Variational Autoencoders
by: Ruoqi Wei, et al.
Published: (2021) -
Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
by: Youngjae Lee, et al.
Published: (2021)