Abstract
Few-shot image classification aims to recognize unseen categories with only a few labeled training samples. Recent metric-based approaches tend to represent each sample with a high-level semantic representation and make decisions according to the similarities between the query sample and support categories. However, high-level concepts are identified to be poor at generalizing to novel concepts that differ from previous seen concepts due to domain shifts. Moreover, most existing methods conduct one-way instance-level metric without involving more discriminative local relations. In this paper, we propose a Local Mutual Metric Network (LM2N), which combines low-level structural representations with high-level semantic representations by unifying all abstraction levels of the embedding network to achieve a balance between discrimination and generalization ability. We also propose a novel local mutual metric strategy to collect and reweight local relations in a bidirectional manner. Extensive experiments on five benchmark datasets (i.e. miniImageNet, tieredImageNet and three fine-grained datasets) show the superiority of our proposed method.
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References
Allen, K., Shelhamer, E., Shin, H., Tenenbaum, J.: Infinite mixture prototypes for few-shot learning. In: ICML, pp. 232–241 (2019)
Antoniou, A., Edwards, H., Storkey, A.: How to train your maml. In: ICLR (2018)
Chen, H., Li, H., Li, Y., Chen, C.: Multi-scale adaptive task attention network for few-shot learning. ar**v preprint ar**v:2011.14479 (2020)
Chen, H., Li, H., Li, Y., Chen, C.: Multi-level metric learning for few-shot image recognition. ar**v preprint ar**v:2103.11383 (2021)
Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B.: A closer look at few-shot classification. In: ICLR (2019)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)
Fe-Fei, L., et al.: A bayesian approach to unsupervised one-shot learning of object categories. In: ICCV, pp. 1134–1141 (2003)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML, pp. 1126–1135 (2017)
Hao, F., He, F., Cheng, J., Wang, L., Cao, J., Tao, D.: Collect and select: semantic alignment metric learning for few-shot learning. In: ICCV, pp. 8460–8469 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Hochreiter, S., Younger, A.S., Conwell, P.R.: Learning to learn using gradient descent. In: International Conference on Artificial Neural Networks, pp. 87–94 (2001)
Hou, R., Chang, H., Ma, B., Shan, S., Chen, X.: Cross attention network for few-shot classification. In: NeurIPS, pp. 4003–4014 (2019)
Huang, H., Zhang, J., Zhang, J., Xu, J., Wu, Q.: Low-rank pairwise alignment bilinear network for few-shot fine-grained image classification. IEEE Trans. Multimedia 23, 1666–1680 (2020)
Khosla, A., Jayadevaprakash, N., Yao, B., Li, F.F.: Novel dataset for fine-grained image categorization: stanford dogs. In: CVPR Workshop on FGVC, vol. 2 (2011)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Koch, G., Zemel, R., Salakhutdinov, R., et al.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: ICCV Workshop, pp. 554–561 (2013)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NeurIPS, vol. 25, pp. 1097–1105 (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Li, W., Wang, L., Huo, J., Shi, Y., Gao, Y., Luo, J.: Asymmetric distribution measure for few-shot learning, pp. 2957–2963 (2020)
Li, W., Wang, L., Xu, J., Huo, J., Gao, Y., Luo, J.: Revisiting local descriptor based image-to-class measure for few-shot learning. In: CVPR, pp. 7260–7268 (2019)
Li, W., Xu, J., Huo, J., Wang, L., Gao, Y., Luo, J.: Distribution consistency based covariance metric networks for few-shot learning. In: AAAI, pp. 8642–8649 (2019)
Li, Y., Li, H., Chen, H., Chen, C.: Hierarchical representation based query-specific prototypical network for few-shot image classification. ar**v preprint ar**v:2103.11384 (2021)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117–2125 (2017)
Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. In: ICLR (2018)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: NeurIPS, vol. 32, pp. 8026–8037 (2019)
Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. In: ICLR (2018)
Satorras, V.G., Estrach, J.B.: Few-shot learning with graph neural networks. In: ICLR (2018)
Simon, C., Koniusz, P., Nock, R., Harandi, M.: Adaptive subspaces for few-shot learning. In: CVPR, pp. 4136–4145 (2020)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: NeurIPS, pp. 4080–4090 (2017)
Sun, Q., Liu, Y., Chua, T.S., Schiele, B.: Meta-transfer learning for few-shot learning. In: CVPR, pp. 403–412 (2019)
Sung, F., Yang, Y., Zhang, L., **ang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR, pp. 1199–1208 (2018)
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J.B., Isola, P.: Rethinking few-shot image classification: a good embedding is all you need? In: ECCV, pp. 266–282 (2020)
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NeurIPS, pp. 3637–3645 (2016)
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In: ECCV, pp. 3–19 (2018)
Zhang, C., Li, H., Chen, C., Qian, Y., Zhou, X.: Enhanced group sparse regularized nonconvex regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https://doi.org/10.1109/TPAMI.2020.3033994
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR, pp. 2881–2890 (2017)
Zhu, W., Li, W., Liao, H., Luo, J.: Temperature network for few-shot learning with distribution-aware large-margin metric. Pattern Recogn. 112, 107797 (2021)
Zhu, Y., Liu, C., Jiang, S.: Multi-attention meta learning for few-shot fine-grained image recognition. In: IJCAI, pp. 1090–1096 (2020)
Acknowledgement
This work was partially supported by National Natural Science Foundation of China (Nos. 62176116, 71732003, 62073160, and 71671086) and the National Key Research and Development Program of China (Nos. 2018YFB1402600).
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Li, Y., Li, H., Chen, H., Chen, C. (2021). Local Mutual Metric Network for Few-Shot Image Classification. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_36
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DOI: https://doi.org/10.1007/978-3-030-88004-0_36
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