One-shot learning of object categories
Abstract
Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by Maximum Likelihood (ML) and Maximum A Posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully.
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Humans (even children) can learn a visual concept quickly from very few given examples and can often generalize well.
“In contrast, humans (even children) can learn a visual concept quickly from very few given examples and can often generalize well (Biederman 1987b;Lake et al. 2015;FeiFei et al. 2006).”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as FeiFei et al. 2006
On the standard Caltech-101 benchmark, initial experiments with the network achieved 50% recognition accuracy with 15 training images and 62% recognition accuracy with 30 training images.
“On the standard Caltech-101 benchmark [60], our initial experiments with the network achieved 50% recognition accuracy with 15 training images and 62% recognition accuracy with 30 training images.”
From Towards a mathematical theory of cortical micro-circuits (2009) · cites this paper as [60]
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- Authors
- Fei-Fei, L., Fergus, R., & Perona, P.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Institute of Electrical and Electronics Engineers (US)
- California Institute of Technology (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-04
- Volume
- 28
- Issue
- 4
- Pages
- 594-611
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2115733720
- Cited by
- 3,029
- References
- 45
- Citations by year
- 2026: 80 · 2025: 163 · 2024: 247 · 2023: 249 · 2022: 288 · 2021: 348 · 2020: 382 · 2019: 285 · 2018: 161 · 2017: 126 · 2016: 74 · 2015: 87 · 2014: 81 · 2013: 80 · 2012: 72
- FWCI
- 54.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- 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
- one-shot learning, object category learning, Bayesian model, probabilistic models, prior knowledge, limited training data, maximum likelihood estimation, maximum a posteriori estimation, visual object recognition
- MeSH terms
- Algorithms, Artificial Intelligence, Bayes Theorem, Computer Simulation, Image Enhancement, Image Interpretation, Computer-Assisted, Models, Biological, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Cluster Analysis, Information Storage and Retrieval, Imaging, Three-Dimensional
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