MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning
Abstract
As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge. In this paper, we concentrate on this "slow versus fast" (SvF) dilemma to determine which knowledge components to be updated in a slow fashion or a fast fashion, and thereby balance old-knowledge preservation and new-knowledge adaptation. We propose a multi-grained SvF learning strategy to cope with the SvF dilemma from two different grains: intra-space (within the same feature space) and inter-space (between two different feature spaces). The proposed strategy designs a novel frequency-aware regularization to boost the intra-space SvF capability, and meanwhile develops a new feature space composition operation to enhance the inter-space SvF learning performance. With the multi-grained SvF learning strategy, our method outperforms the state-of-the-art approaches by a large margin.
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FSCIL has two main frameworks: one uses a feature extractor with a softmax classifier, and the other involves a feature embedding network and the nearest class mean classifier.
“FSCIL has two main frameworks, as shown in Fig. 6. The first uses a feature extractor with a softmax classifier, while the second involves a feature embedding network and the nearest class mean classifier [22, 42, 43].”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [22]
Low-frequency information may contribute more to preserving old knowledge.
“It was found that low-frequency information may contribute more to preserving old knowledge.”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [22]
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Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey
Zhang, J., Liu, L., Silvén, O., Pietikäinen, M., & Hu, D. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3529038 · Cited by 84 · see citing works
Few-Shot Class-Incremental Learning for Retinal Disease Recognition
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- Authors
- Zhao, H., Fu, Y., Kang, M., Tian, Q., Wu, F., & Li, X.
- Affiliations
- Zhejiang University (CN)
- Huawei Technologies (China) (CN)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2024-03
- Volume
- 46
- Issue
- 3
- Pages
- 1576-1588
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3113584014
- Cited by
- 115
- References
- 10
- Citations by year
- 2026: 18 · 2025: 47 · 2024: 32 · 2023: 11 · 2022: 5 · 2021: 2
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (2020AAA0107400)
- National Natural Science Foundation of China (U20A20222, 61972071, U20A20184)
- Zhejiang Provincial Natural Science Foundation of China (LR19F020004)
- Key Scientific Technological Innovation Research Project
- Zhejiang University K.P.Chao's High Technology Development Foundation
- Zhejiang Lab's Open Fund (2020AA3AB14)
- Zhejiang Laboratory (2019KD0AB02)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- few-shot class-incremental learning, catastrophic forgetting, continual learning, knowledge preservation
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