.png){kind=icon}
返回
A Progressive Multi-Scale Relation Network for Few-Shot Image Classification
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3414346.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Few-shot classification addresses the challenge of swiftly enabling a deep learning model to comprehend new classes based on minimal supporting image samples. Despite recent research predominantly focusing on devising increasingly intricate classifiers for gauging similarities between query and support images, there is room for improvement in the critical role of feature embeddings and relational measurements. In this paper, a novel technique called Progressive Multi-Scale Relation Network (PMRNet), is designed for few-shot classification tasks. Our work introduces a feature extraction module that adeptly extracts and interactively fuses multi-scale features to address the problem of insufficient feature extraction. Furthermore, an enhanced attention mechanism is integrated, which amplifies critical local and global features for increased discriminative power. A novel bilinear relation module is also designed specifically to accurately quantify the similarities between the extracted features. The PMRNet has been rigorously evaluated on three benchmark datasets. PMRNet's empirical performance in few-shot image classification tasks exhibits commendable accuracy, substantiating its efficacy as a robust architectural framework for significantly boosting the precision of such tasks.
Keyword:
Feature extraction
Task analysis
Image classification
Computational modeling
Semantics
Kernel
Convolutional neural networks
Few shot learning
Few-shot learning
image classification
metric learning
multi-scales feature
attention module
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
A Multiscale Spatial-Spectral Prototypical Network for Hyperspectral Image Few-Shot Classification用于高光谱图像少镜头分类的多尺度空间光谱原型网络
PREAI
分享收藏Discriminant space metric network for few-shot image classification用于少镜头图像分类的判别空间度量网络.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏
分享收藏
查看更多内容