원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 8th International Conference on Next Generation Computing 2022
2022.10
pp.169-172
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
The goal of few-shot learning is to use limited labeled samples to achieve effective classification results. To mine the features of images in the limited number of samples, some researchers proposed to mine salient features to improve the classification effect. However, they ignore the use of secondary salient features. Therefore, we propose to use secondary salient features to supplement the deficiency of salient features. Combining with the foreground extraction network and the graph neural network, a better classification effect is obtained in the experiment.
목차
Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Problem definition
B. Overall Framework
C. Loss
III. EXPERIMENTS
A. Datasets
B. Few-Shot Classification
IV. CONCLUSION
REFERENCES
I. INTRODUCTION
II. METHODOLOGY
A. Problem definition
B. Overall Framework
C. Loss
III. EXPERIMENTS
A. Datasets
B. Few-Shot Classification
IV. CONCLUSION
REFERENCES
저자정보
참고문헌
자료제공 : 네이버학술정보