Low-Shot Learning with Imprinted Weights
Open accessAbstract
Human vision is able to immediately recognize novel visual categories after seeing just one or a few training examples. We describe how to add a similar capability to ConvNet classifiers by directly setting the final layer weights from novel training examples during low-shot learning. We call this process weight imprinting as it directly sets weights for a new category based on an appropriately scaled copy of the embedding layer activations for that training example. The imprinting process provides a valuable complement to training with stochastic gradient descent, as it provides immediate good classification performance and an initialization for any further fine-tuning in the future. We show how this imprinting process is related to proxy-based embeddings. However, it differs in that only a single imprinted weight vector is learned for each novel category, rather than relying on a nearest-neighbor distance to training instances as typically used with embedding methods. Our experiments show that using averaging of imprinted weights provides better generalization than using nearest-neighbor instance embeddings.
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The practical need for learning new classes from limited samples without compromising performance on previous classes has led to the development of gFSL.
“This practical need has led to the development of a novel setting, gFSL [15], aimed at enabling learning of new classes with limited samples without compromising performance on previous classes [15, 55, 56].”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [55]
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- Authors
- Qi, H., Brown, M., & Lowe, D. G.
- Affiliations
- University of California, Los Angeles (US)
- Google (United States) (US)
- ORCID
- Lowe, David G.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Volume
- 2018
- Pages
- 5822-5830
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2796346823
- Cited by
- 539
- References
- 26
- Citations by year
- 2026: 12 · 2025: 31 · 2024: 58 · 2023: 78 · 2022: 97 · 2021: 119 · 2020: 99 · 2019: 42 · 2018: 3
- FWCI
- 26.74
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE Conference Proceedings
- 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 learning, novel category recognition, generalization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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