The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers
Open accessAbstract
In this paper, we address the few-shot classification task from a new perspective of optimal matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance. The EMD generates the optimal matching flows between structural elements that have the minimum matching cost, which is used to represent the image distance for classification. To generate the important weights of elements in the EMD formulation, we design a cross-reference mechanism, which can effectively minimize the impact caused by the cluttered background and large intra-class appearance variations. To handle k-shot classification, we propose to learn a structured fully connected layer that can directly classify dense image representations with the EMD. Based on the implicit function theorem, the EMD can be inserted as a layer into the network for end-to-end training. We conduct comprehensive experiments to validate our algorithm and we set new state-of-the-art performance on four popular few-shot classification benchmarks, namely miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100) and Caltech-UCSD Birds-200-2011 (CUB).
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
Dynamic Few-Shot Visual Learning Without Forgetting
Gidaris, S., & Komodakis, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00459 · Cited by 1,128
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Schönfeld, E., Ebrahimi, S., Sinha, S., Darrell, T., & Akata, Z. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00844 · Cited by 747
Low-Shot Learning with Imprinted Weights
Qi, H., Brown, M., & Lowe, D. G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00610 · Cited by 539
Cited by
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Song, Y., Wang, T., Cai, P., Mondal, S. K., & Sahoo, J. P. (2023) · ACM Computing Surveys · doi:10.1145/3582688 · Cited by 721
Few-Shot Incremental Learning with Continually Evolved Classifiers
Zhang, C., Song, N., Lin, G., Zheng, Y., Pan, P., & Xu, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01227 · Cited by 382
Self-citationDeep 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
Constrained Few-shot Class-incremental Learning
Hersche, M., Karunaratne, G., Cherubini, G., Benini, L., Sebastian, A., & Rahimi, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00885 · Cited by 167
Fine-grained Angular Contrastive Learning with Coarse Labels
Bukchin, G., Schwartz, E., Saenko, K., Shahar, O., Feris, R., Giryes, R., & Karlinsky, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00862 · Cited by 55
Metadata
- Authors
- Zhang, C., Cai, Y., Lin, G., & Shen, C.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 12200-12210
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3012255272
- Cited by
- 898
- References
- 7
- FWCI
- 51.60
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, C., Cai, Y., Lin, G., & Shen, C. (2020). DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.01222
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