Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2022

Abstract

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the target domain. To help knowledge transfer, this paper introduces an intermediate domain generated by mixing images in the source and the target domain. Specifically, to generate the optimal intermediate domain for different target data, we propose a novel target guided dynamic mixup (TGDM) framework that leverages the target data to guide the generation of mixed images via dynamic mixup. The proposed TGDM framework contains a Mixup-3T network for learning classifiers and a dynamic ratio generation network (DRGN) for learning the optimal mix ratio. To better transfer the knowledge, the proposed Mixup-3T network contains three branches with shared parameters for classifying classes in the source domain, target domain, and intermediate domain. To generate the optimal intermediate domain, the DRGN learns to generate an optimal mix ratio according to the performance on auxiliary target data. Then, the whole TGDM framework is trained via bi-level meta-learning so that TGDM can rectify itself to achieve optimal performance on target data. Extensive experimental results on several benchmark datasets verify the effectiveness of our method

Keywords

cross-domain few-shot learning, dynamic mixup, target guided learning, bi-level meta-learning

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, Portugal, 2022 October 10-14

First Page

6368

Last Page

6376

ISBN

9781450392037

Identifier

10.1145/3503161.3548052

Publisher

ACM

City or Country

Lisboa, Portugal

Additional URL

http://doi.org/10.1145/3503161.3548052

Download

Share

COinS