Task-Oriented Local Feature Rectification Network for Few-Shot Image Classification

Ping Li () and Xiang Zhu
Additional contact information
Ping Li: School of Computer Science, Nanjing University of Posts and Telecommunications, No. 9 Wenyuan Road, Nanjing 210023, China
Xiang Zhu: School of Computer Science, Nanjing University of Posts and Telecommunications, No. 9 Wenyuan Road, Nanjing 210023, China

Mathematics, 2025, vol. 13, issue 9, 1-15

Abstract: Few-shot image classification aims to classify unlabeled samples when only a small number of labeled samples are available for each class. Recently, local feature-based few-shot learning methods have made significant progress. However, existing methods often treat all local descriptors equally, without considering the importance of each local descriptor in different tasks. Therefore, the few-shot learning model is easily disturbed by class-irrelevant features, which results in a decrease in accuracy. To address this issue, we propose a task-oriented local feature rectification network (TLFRNet) with two feature rectification modules (support rectification module and query rectification module). The former module uses the relationship between each local descriptor and prototypes within the support set to rectify the support features. The latter module uses a CNN to rectify the similarity tensors between the query and support local features and then models the importance of the query local features. Through these two modules, our model can effectively reduce the intra-class variation of class-relevant features, thus obtaining more accurate image-to-class similarity for classification. Extensive experiments on five datasets show that TLFRNet achieves more superior classification performance than the related methods.

Keywords: few-shot learning; image classification; local feature; feature rectification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/9/1519/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/9/1519/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:9:p:1519-:d:1649375

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().