@inproceedings{4d5a6e14d620445a88ce34844952cad6,
title = "Multi-Level Relation Learning with Confidence Evaluation for Few-Shot Learning",
abstract = "Few-shot learning is developed to classify unknown categories through limited training samples. In this paper, a multi-level relation learning model with confidence evaluation (MLR-CE) is proposed in area of few-shot learning. In the proposed framework, multi-level features are extracted that contain semantic information of different depth, and multi-level relation pairs are built by stacking feature maps of support images and query images. To expand the support set, confidence evaluation by a Gaussian mixture model is developed to select samples with high confidences. Experiments on two data demonstrate that the proposed method can yield superior few-shot classification results.",
keywords = "Confidence Evaluation, Few-Shot Learning, Multi-Level, Relation Learning",
author = "Qingjie Zeng and Jie Geng and Kai Huang and Wen Jiang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",
year = "2021",
doi = "10.1109/CCDC52312.2021.9602742",
language = "英语",
series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1608--1612",
booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
}