Multi-level Attention Feature Network for Few-shot Learning

Ronggui WANG; Mengya HAN; Juan YANG; Lixia XUE; Min HU

doi:10.11999/JEIT190242

Volume 42 Issue 3

Mar. 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(3): 772-778

Ronggui WANG, Mengya HAN, Juan YANG, Lixia XUE, Min HU. Multi-level Attention Feature Network for Few-shot Learning[J]. Journal of Electronics & Information Technology, 2020, 42(3): 772-778. doi: 10.11999/JEIT190242

Citation:

Ronggui WANG, Mengya HAN, Juan YANG, Lixia XUE, Min HU. Multi-level Attention Feature Network for Few-shot Learning[J]. Journal of Electronics & Information Technology, 2020, 42(3): 772-778. doi: 10.11999/JEIT190242

Citation:

PDF( 1656 KB)

Multi-level Attention Feature Network for Few-shot Learning

doi: 10.11999/JEIT190242

School of Computer and Information, Hefei University of Technology, Hefei 230009, China

Funds: The National Natural Science Foundation of China (61672202), The State Key Program of National Natural Science Foundation of China-Shenzhen Joint Foundation (U1613217)

Received Date: 2019-04-11
Rev Recd Date: 2019-09-05

Available Online: 2019-09-17

Publish Date: 2020-03-19

Abstract

Abstract

Existing few-shot methods have problems that feature extraction scale is single, the learned class representations are inaccurate, the similarity calculation still relies on standard metrics. In order to solve the above problems, multi-level attention feature network is proposed. Firstly, the multiple scale images are obtained by scale processing, the features of multiple scale images are extracted and the image-level attention features are obtained by the image-level attention mechanism to fusion them. Then, class-level attention features are learned by using the class-level attention mechanism. Finally, the classification is performed by using the network to compute the similarity scores between features. The proposed method is evaluated on the Omniglot dataset and the MiniImagenet dataset. The experimental results show that multi-level attention feature network can further improve the classification accuracy under small sample conditions compared to the single-scale image features and average prototypes.
- Image processing,
- Multi-scale images,
- Few-shot learning,
- Multi-level attention feature,
- Similarity metric

FullText(HTML)

References(16)

References

GIRSHICK R. Fast R-CNN[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1440–1448. doi: 10.1109/ICCV.2015.169.

HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2261–2269. doi: 10.1109/CVPR.2017.243.

HE Di, XIA Yingce, QIN Tao, et al. Dual learning for machine translation[C]. The 30th Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 820–828.

LI Feifei, FERGUS R, and PERONA P. One-shot learning of object categories[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 594–611. doi: 10.1109/TPAMI.2006.79

MEHROTRA A and DUKKIPATI A. Generative adversarial residual pairwise networks for one shot learning[EB/OL]. https://arxiv.org/abs/1703.08033, 2017.

DIXIT M, KWITT R, NIETHAMMER M, et al. AGA: Attribute-guided augmentation[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 7455–7463. doi: 10.1109/CVPR.2017.355.

HARIHARAN B and GIRSHICK R. Low-shot visual recognition by shrinking and hallucinating features[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 3037–3046. doi: 10.1109/iccv.2017.328.

FINN C, ABBEEL P, and LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]. The 34th International Conference on Machine Learning, Sydney, Australia, 2017: 1126–1135.

RAVI S and LAROCHELLE H. Optimization as a model for few-shot learning[EB/OL]. https://openreview.net/forum?id=rJY0-Kcll, 2017.

SANTORO A, BARTUNOV S, BOTVINICK M, et al. Meta-learning with memory-augmented neural networks[C]. The 33rd International Conference on Machine Learning, New York, USA, 2016: 1842–1850.

KOCH G. Siamese neural networks for one-shot image recognition[EB/OL]. http://www.cs.utoronto.ca/~gkoch/files/msc-thesis.pdf, 2015.

VINYALS O, BLUNDELL C, LILLICRAP T, et al. Matching networks for one shot learning[C]. The 30th Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 3630–3638.

SNELL J, SWERSKY K, and ZEMEL R. Prototypical networks for few-shot learning[C]. The 31st Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 4080–4090.

SUNG F, YANG Yongxin, ZHANG Li, et al. Learning to compare: Relation network for few-shot learning[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 1199–1208. doi: 10.1109/cvpr.2018.00131.

WANG Peng, LIU Lingqiao, and SHEN Chunhua. Multi-attention network for one shot learning[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 6212–6220. doi: 10.1109/CVPR.2017.658.

HILLIARD N, HODAS N O, and CORLEY C D. Dynamic input structure and network assembly for few-shot learning[EB/OL]. https://arxiv.org/abs/1708.06819v1, 2017.

Relative Articles

Supplements(0)

Cited By

Proportional views