Cargando…
Few-Shot Fine-Grained Image Classification via GNN
Traditional deep learning methods such as convolutional neural networks (CNN) have a high requirement for the number of labeled samples. In some cases, the cost of obtaining labeled samples is too high to obtain enough samples. To solve this problem, few-shot learning (FSL) is used. Currently, typic...
Autores principales: | Zhou, Xiangyu, Zhang, Yuhui, Wei, Qianru |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571755/ https://www.ncbi.nlm.nih.gov/pubmed/36236743 http://dx.doi.org/10.3390/s22197640 |
Ejemplares similares
-
Feature fusion network based on few-shot fine-grained classification
por: Yang, Yajie, et al.
Publicado: (2023) -
Hybrid Fine-Tuning Strategy for Few-Shot Classification
por: Zhao, Lei, et al.
Publicado: (2022) -
Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning
por: Sun, Bingjian, et al.
Publicado: (2022) -
Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification
por: Chen, Zhikui, et al.
Publicado: (2021) -
Weakly Correlated Knowledge Integration for Few-shot Image Classification
por: Yang, Chun, et al.
Publicado: (2022)