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More Attentional Local Descriptors for Few-Shot Learning

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Artificial Neural Networks and Machine Learning – ICANN 2020 (ICANN 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12396))

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Abstract

Learning from a few examples remains a key challenge for many computer vision tasks. Few-shot learning is proposed to tackle this problem. It aims to learn a classifier to classify images when each class contains only few samples with supervised information in image classification. So far, existing methods have achieved considerable progress, which use fully connected layer or global average pooling as the final classification method. However, due to the lack of samples, global feature may no longer be useful. In contrast, the local feature is more conductive to few-shot learning, but inevitably there will be some noises. In the meanwhile, inspired by human visual systems, the attention mechanism can obtain more valuable information and be widely used in various areas. Therefore, in this paper, we propose a method called More Attentional Deep Nearest Neighbor Neural Network (MADN4 in short) that combines the local descriptors with attention mechanism and is trained end-to-end from scratch. The experimental results on four benchmark datasets demonstrate the superior capability of our method.

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References

  1. Aditya, K., Nityananda, J., Bangpeng, Y., Li, F.: Novel dataset for fine-grained image categorization. In: Proceedings of the First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Springs, USA (2011)

    Google Scholar 

  2. Bertinetto, L., Henriques, J.F., Valmadre, J., Torr, P., Vedaldi, A.: Learning feed-forward one-shot learners. In: Advances in neural information processing systems, pp. 523–531 (2016)

    Google Scholar 

  3. Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008). https://doi.org/10.1109/CVPR.2008.4587598

  4. Chen, Z., Fu, Y., Zhang, Y., Jiang, Y.G., Xue, X., Sigal, L.: Semantic feature augmentation in few-shot learning. arXiv preprint arXiv:1804.05298 (2018)

  5. Chu, W.H., Wang, Y.C.F.: Learning semantics-guided visual attention for few-shot image classification. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2979–2983. IEEE (2018). https://doi.org/10.1109/ICIP.2018.8451350

  6. Dixit, M., Kwitt, R., Niethammer, M., Vasconcelos, N.: AGA: attribute-guided augmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7455–7463 (2017). https://doi.org/10.1109/CVPR.2017.355

  7. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1126–1135. JMLR. org (2017)

    Google Scholar 

  8. Garcia, V., Bruna, J.: Few-shot learning with graph neural networks. arXiv preprint arXiv:1711.04043 (2017)

  9. Goodfellow, I., et al..: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  10. Hariharan, B., Girshick, R.: Low-shot visual recognition by shrinking and hallucinating features. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3018–3027 (2017). https://doi.org/10.1109/ICCV.2017.328

  11. Hou, R., Chang, H., Bingpeng, M., Shan, S., Chen, X.: Cross attention network for few-shot classification. In: Advances in Neural Information Processing Systems, pp. 4005–4016 (2019)

    Google Scholar 

  12. Hui, B., Zhu, P., Hu, Q., Wang, Q.: Self-attention relation network for few-shot learning. In: 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 198–203. IEEE (2019). https://doi.org/10.1109/ICMEW.2019.00041

  13. Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, vol. 2. Lille (2015)

    Google Scholar 

  14. Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554–561 (2013). https://doi.org/10.1109/ICCVW.2013.77

  15. 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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7260–7268 (2019). https://doi.org/10.1109/cvpr.2019.00743

  16. Lifchitz, Y., Avrithis, Y., Picard, S., Bursuc, A.: Dense classification and implanting for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9258–9267 (2019). https://doi.org/10.1109/CVPR.2019.00948

  17. Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141 (2017)

  18. Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. In: ICLR (2018)

    Google Scholar 

  19. Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. In: Advances in neural information processing systems, pp. 2204–2212 (2014)

    Google Scholar 

  20. Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7229–7238 (2018). https://doi.org/10.1109/CVPR.2018.00755

  21. Sachin, R., Hugo, L.: Optimization as a model for few-shot learning. In: ICLR (2017)

    Google Scholar 

  22. Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International conference on machine learning, pp. 1842–1850 (2016)

    Google Scholar 

  23. Schwartz, E., : Delta-encoder: an effective sample synthesis method for few-shot object recognition. In: Advances in Neural Information Processing Systems, pp. 2845–2855 (2018)

    Google Scholar 

  24. Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077–4087 (2017)

    Google Scholar 

  25. Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018). https://doi.org/10.1109/CVPR.2018.00131

  26. Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3630–3638 (2016)

    Google Scholar 

  27. Wang, P., Liu, L., Shen, C., Huang, Z., van den Hengel, A., Tao Shen, H.: Multi-attention network for one shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2721–2729 (2017). https://doi.org/10.1109/CVPR.2017.658

  28. Wang, Y.X., Girshick, R., Hebert, M., Hariharan, B.: Low-shot learning from imaginary data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7278–7286 (2018). https://doi.org/10.1109/CVPR.2018.00760

  29. Welinder, P., et al..: Caltech-ucsd birds 200 (2010)

    Google Scholar 

  30. Woo, S., Park, J., Lee, J.Y., So Kweon, I.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018). https://doi.org/10.1007/978-3-030-01234-2_1

  31. Yan, S., Zhang, S., He, X., et al.: A dual attention network with semantic embedding for few-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9079–9086 (2019)

    Google Scholar 

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant 61732011 and Grant 61702358, in part by the Beijing Natural Science Foundation under Grant Z180006, in part by the Key Scientific and Technological Support Project of Tianjin Key Research and Development Program under Grant 18YFZCGX00390, and in part by the Tianjin Science and Technology Plan Project under Grant 19ZXZNGX00050.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Hui Li, Liu Yang & Fei Gao

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  1. Hui Li
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  2. Liu Yang
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  3. Fei Gao
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Correspondence to Liu Yang .

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Editors and Affiliations

  1. Department of Applied Informatics, Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

    Paolo Masulli

  3. Department of Informatics, University of Hamburg, Hamburg, Germany

    Stefan Wermter

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Li, H., Yang, L., Gao, F. (2020). More Attentional Local Descriptors for Few-Shot Learning. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_33

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  • DOI: https://doi.org/10.1007/978-3-030-61609-0_33

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