City Research Online

Abstract

Few-shot fine-grained image classification is prominent but challenging in computer vision, aiming to distinguish sub-classes under the same parent class but with only a few labeled support samples. Data augmentation techniques were explored to address the few-shot issue, but they often fail to mitigate the bias between support and query samples. Therefore, in this paper we propose a query-aware cross-mixup and cross-reconstruction method to address both few-shot and fine-grained issues. Specifically, in the training phase, we randomly select query samples and mix them with the support samples from the same class to augment the support set. This first strategy ensures the augmented support set query-aware within each sub-class. Then, we reconstruct both query samples and support samples from both original and cross-mixed support samples, thus leveraging both cross-reconstruction and self-reconstruction to enhance classification. This second strategy, enabling the reconstruction also query-aware, further mitigates the bias between support and query samples, leading to more reliable generalization. We evaluate our proposed method on four widely used few-shot fine-grained image classification datasets, and experimental results demonstrate its effectiveness in achieving the state-of-the-art classification performance.

Publication Type:	Article
Additional Information:	© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	Data augmentation, Few-shot image classification, Fine-grained image classification
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Bayes Business School Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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