The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Multi-Scale Metric Learning for Few-Shot Learning
Abstract
Few-shot learning in image classification is developed to learn a model that aims to identify unseen classes with only few training samples for each class. Fewer training samples and new tasks of classification make many traditional classification models no longer applicable. In this paper, a novel few-shot learning method named multi-scale metric learning (MSML) is proposed to extract multi-scale features and learn the multi-scale relations between samples for the classification of few-shot learning. In the proposed method, a feature pyramid structure is introduced for multi-scale feature embedding, which aims to combine high-level strong semantic features with low-level but abundant visual features. Then a multi-scale relation generation network (MRGN) is developed for hierarchical metric learning, in which high-level features are corresponding to deeper metric learning while low-level features are corresponding to lighter metric learning. Moreover, a novel loss function named intra-class and inter-class relation loss (IIRL) is proposed to optimize the proposed deep network, which aims to strengthen the correlation between homogeneous groups of samples and weaken the correlation between heterogeneous groups of samples. Experimental results on mini ImageNet and tiered ImageNet demonstrate that the proposed method achieves superior performance in few-shot learning problem.
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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
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
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
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Learning to Learn: Introduction and Overview
Thrun, S., & Pratt, L. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_1 · Cited by 521
Video-based emotion recognition in the wild using deep transfer learning and score fusion
Kaya, H., Gürpınar, F., & Salah, A. A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.012 · Cited by 235 · cited 1×
“…last layer with replacing the new softmax layers that are relevant to our own task, the universal features extracted by the pre-trained network that are also relevant to the new tasks are reserved, and the network will focus on learning dataset-specific features in the subsequent layers [39], [40].”
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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
Meta-Learning-Based Incremental Few-Shot Object Detection
Cheng, M., Wang, H., & Long, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3088545 · Cited by 111
Entropy-based redundancy analysis and information screening
Li, Y., Yang, J., & Wen, J. (2023) · Digital Communications and Networks · doi:10.1016/j.dcan.2021.12.001 · Cited by 77
Metadata
- Authors
- Jiang, W., Huang, K., Geng, J., & Deng, X.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2021-03
- Volume
- 31
- Issue
- 3
- Pages
- 1091-1102
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W3027585699
- Cited by
- 298
- References
- 10
- FWCI
- 15.44
- Type
- journal article
- Language
- en
- Open access
- closed
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