Self-attention network for few-shot learning based on nearest-neighbor algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://link.springer.com/content/pdf/10.1007/s00138-023-01375-5.pdf
Reference50 articles.
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2. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
3. Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
4. Käding, C., Rodner, E., Freytag, A., Denzler, J.: Fine-tuning deep neural networks in continuous learning scenarios. In: Asian Conference on Computer Vision, pp. 588–605. Springer (2016)
5. Wang, Y., Hu, C., Wang, G., Lin, X.: Contrastive representation for few-shot vehicle footprint recognition. In: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6. IEEE (2021)
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