The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Improved Few-Shot Visual Classification
Open accessAbstract
Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data. Most few-shot learning approaches to date have focused on progressively more complex neural feature extractors and classifier adaptation strategies, and the refinement of the task definition itself. In this paper, we explore the hypothesis that a simple class-covariance-based distance metric, namely the Mahalanobis distance, adopted into a state of the art few-shot learning approach (CNAPS) can, in and of itself, lead to a significant performance improvement. We also discover that it is possible to learn adaptive feature extractors that allow useful estimation of the high dimensional feature covariances required by this metric from surprisingly few samples. The result of our work is a new ``Simple CNAPS'' architecture which has up to 9.2% fewer trainable parameters than CNAPS and performs up to 6.1% better than state of the art on the standard few-shot image classification benchmark dataset.
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Cites
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Automated Flower Classification over a Large Number of Classes
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Human-level concept learning through probabilistic program induction
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Film: Visual reasoning with a general conditioning layer
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Describing Textures in the Wild
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On First-Order Meta-Learning Algorithms
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Dynamic Few-Shot Visual Learning Without Forgetting
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A Comprehensive Survey of Deep Learning for Image Captioning
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Cited by
Deep metric learning for few-shot image classification: A Review of recent developments
Li, X., Yang, X., Ma, Z., & Xue, J.-H. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109381 · Cited by 243
Object Detection with Transformers: A Review
Shehzadi, T., Hashmi, K. A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2025) · Sensors · doi:10.3390/s25196025 · Cited by 93
Metadata
- Authors
- Bateni, P., Goyal, R., Masrani, V., Wood, F., & Sigal, L.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 14481-14490
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2993323518
- Cited by
- 239
- References
- 12
- FWCI
- 14.31
- Type
- conference paper
- Language
- en
- Open access
- green
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- Bateni, P., Goyal, R., Masrani, V., Wood, F., & Sigal, L. (2020). Improved Few-Shot Visual Classification. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.01450
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