SSFE-Net: self-supervised feature enhancement for ultra-fine-grained few-shot class incremental learning

Zicheng Pan*, Xiaohan Yu, Miaohua Zhang, Yongsheng Gao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

5 Citations (Scopus)

Abstract

Ultra-Fine-Grained Visual Categorization (ultra-FGVC) has become a popular problem due to its great real-world potential for classifying the same or closely related species with very similar layouts. However, there present many challenges for the existing ultra-FGVC methods, firstly there are always not enough samples in the existing ultraFGVC datasets based on which the models can easily get overfitting. Secondly, in practice, we are likely to find new species that we have not seen before and need to add them to existing models, which is known as incremental learning. The existing methods solve these problems by Few-Shot Class Incremental Learning (FSCIL), but the main challenge of the FSCIL models on ultra-FGVC tasks lies in their inferior discrimination detection ability since they usually use low-capacity networks to extract features, which leads to insufficient discriminative details extraction from ultrafine-grained images. In this paper, a self-supervised feature enhancement for the few-shot incremental learning network (SSFE-Net) is proposed to solve this problem. Specifically, a self-supervised learning (SSL) and knowledge distillation (KD) framework is developed to enhance the feature extraction of the low-capacity backbone network for ultra-FGVC few-shot class incremental learning tasks. Besides, we for the first time create a series of benchmarks for FSCIL tasks on two public ultra-FGVC datasets and three normal finegrained datasets, which will facilitate the development of the Ultra-FGVC community. Extensive experimental results on public ultra-FGVC datasets and other state-of-the-art benchmarks consistently demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2023 IEEE Winter Conference on Applications of Computer Vision WACV 2023
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages6264-6273
Number of pages10
ISBN (Electronic)9781665493468
ISBN (Print)9781665493475
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 - Waikoloa, United States
Duration: 3 Jan 20237 Jan 2023

Publication series

Name
ISSN (Print)2472-6737
ISSN (Electronic)2642-9381

Conference

Conference23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023
Country/TerritoryUnited States
CityWaikoloa
Period3/01/237/01/23

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