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Few-shot Learning aims to learn and distinguish new categories with a very limited number of available images, presenting a significant challenge in the realm of deep learning.
Jackendoff, R.: On beyond zebra: The relation of linguistic and visual information. Cognition 26
1987
Earlier work this paper cites.
Smith, L., Gasser, M.: The development of embodied cognition: Six lessons from babies. Artificial life 11
2005
Earlier work this paper cites.
Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE transactions on pattern analysis and machine intelligence 28
2006
Earlier work this paper cites.
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9
2008
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). pp. 1532–1543 (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350
2015
Earlier work this paper cites.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. pp. 770–778. IEEE Computer Society (2016)
2016
Earlier work this paper cites.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2016)
2016
Earlier work this paper cites.
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
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, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017. Proceedings of Machine Learning Research, vol. 70, pp. 1126–1135. PMLR (2017)
2017
Earlier work this paper cites.
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
Earlier work this paper cites.
Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. pp. 2999–3007. IEEE Computer Society (2017)
2017
Earlier work this paper cites.
Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. pp. 4077–4087 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. pp. 5998–6008 (2017)
2017
Earlier work this paper cites.
Furlanello, T., Lipton, Z., Tschannen, M., Itti, L., Anandkumar, A.: Born again neural networks. In: International Conference on Machine Learning. pp. 1607–1616. PMLR (2018)
2018
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2018)
2018
Earlier work this paper cites.
Oreshkin, B., Rodríguez López, P., Lacoste, A.: Tadam: Task dependent adaptive metric for improved few-shot learning. Advances in neural information processing systems 31
2018
Earlier work this paper cites.
Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J.B., Larochelle, H., Zemel, R.S.: Meta-learning for semi-supervised few-shot classification. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net (2018)
2018
Earlier work this paper cites.
Satorras, V.G., Estrach, J.B.: Few-shot learning with graph neural networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net (2018)
2018
Cited alongside, same era.
Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4320–4328 (2018)
2018
Cited alongside, same era.
Chen, W., Liu, Y., Kira, Z., Wang, Y.F., Huang, J.: A closer look at few-shot classification. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net (2019)
2019
Cited alongside, same era.
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for nlp. In: International Conference on Machine Learning. pp. 2790–2799. PMLR (2019)
2019
Afrasiyabi, A., Lalonde, J.F., Gagné, C.: Mixture-based feature space learning for few-shot image classification. In: Proc. of ICCV (2021)
2021
Later among the works it cites.
Chen, Y., Liu, Z., Xu, H., Darrell, T., Wang, X.: Meta-baseline: Exploring simple meta-learning for few-shot learning. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9062–9071 (2021)
2021
Later among the works it cites.
Chen, Z., Xie, L., Niu, J., Liu, X., Wei, L., Tian, Q.: Visformer: The vision-friendly transformer. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 589–598 (2021)
2021
Later among the works it cites.
Liu, S., Xie, Y., Yuan, W., Ma, L.: Cross-modality graph neural network for few-shot learning. In: 2021 IEEE International Conference on Multimedia and Expo (ICME). pp. 1–6. IEEE (2021)
2021
Later among the works it cites.
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alphaXiv is searching for related work…
Cited alongside, same era.
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: A lite bert for self-supervised learning of language representations. In: International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10649–10657. IEEE Computer Society. https://doi.org/10.1109/CVPR.2019.01091, https://www.computer.org/csdl/proceedings-article/cvpr/2019/329300k0649/1gys02FFrr2
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Peng, Z., Li, Z., Zhang, J., Li, Y., Qi, G.J., Tang, J.: Few-shot image recognition with knowledge transfer. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 441–449 (2019)
2019
Cited alongside, same era.
Xing, C., Rostamzadeh, N., Oreshkin, B., O Pinheiro, P.O.: Adaptive cross-modal few-shot learning. Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., Ma, K.: Be your own teacher: Improve the performance of convolutional neural networks via self distillation. In: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. pp. 3712–3721. IEEE (2019)
2019
Cited alongside, same era.
Afrasiyabi, A., Lalonde, J.F., Gagné, C.: Associative alignment for few-shot image classification. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16. pp. 18–35. Springer (2020)
2020
Cited alongside, same era.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Cited alongside, same era.
Popham, S.F., Huth, A.G., Bilenko, N.Y., Deniz, F., Gao, J.S., Nunez-Elizalde, A.O., Gallant, J.L.: Visual and linguistic semantic representations are aligned at the border of human visual cortex. Nature neuroscience 24
2021
Later among the works it cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Later among the works it cites.
Wang, H., Zhao, H., Li, B.: Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation. In: International Conference on Machine Learning. pp. 10991–11002. PMLR (2021)
2021
Later among the works it cites.
Wertheimer, D., Tang, L., Hariharan, B.: Few-shot classification with feature map reconstruction networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8012–8021 (2021)
2021
Later among the works it cites.
Yan, K., Bouraoui, Z., Wang, P., Jameel, S., Schockaert, S.: Aligning visual prototypes with bert embeddings for few-shot learning. In: Proceedings of the 2021 International Conference on Multimedia Retrieval. pp. 367–375 (2021)
2021
Later among the works it cites.
Afrasiyabi, A., Larochelle, H., Lalonde, J.F., Gagné, C.: Matching feature sets for few-shot image classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9014–9024 (2022)
2022
Later among the works it cites.
Dong, B., Zhou, P., Yan, S., Zuo, W.: Self-promoted supervision for few-shot transformer. In: European Conference on Computer Vision. pp. 329–347. Springer (2022)
2022
Later among the works it cites.
Pourpanah, F., Abdar, M., Luo, Y., Zhou, X., Wang, R., Lim, C.P., Wang, X.Z., Wu, Q.J.: A review of generalized zero-shot learning methods. IEEE transactions on pattern analysis and machine intelligence 45
2022
Later among the works it cites.
Yu, T., He, S., Song, Y.Z., Xiang, T.: Hybrid graph neural networks for few-shot learning. In: Proc. of AAAI (2022)
2022
Later among the works it cites.
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16816–16825 (2022)
2022
Later among the works it cites.
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. International Journal of Computer Vision 130
2022
Later among the works it cites.
Chen, W., Si, C., Zhang, Z., Wang, L., Wang, Z., Tan, T.: Semantic prompt for few-shot image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23581–23591 (2023)
2023
Later among the works it cites.
He, J., Kortylewski, A., Yuille, A.: Corl: Compositional representation learning for few-shot classification. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 3890–3899 (January 2023)
2023
Later among the works it cites.
OpenAI: Gpt-4 technical report (2023)
2023
Later among the works it cites.
Song, Y., Wang, T., Cai, P., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. ACM Computing Surveys (2023)
2023
Later among the works it cites.
Sun, S., Gao, H.: Meta-adam: An meta-learned adaptive optimizer with momentum for few-shot learning. In: Advances in Neural Information Processing Systems. vol. 36, pp. 65441–65455 (2023)
2023
Later among the works it cites.
Yang, F., Wang, R., Chen, X.: Semantic guided latent parts embedding for few-shot learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 5447–5457 (2023)
2023
Later among the works it cites.
Yao, H., Zhang, R., Xu, C.: Visual-language prompt tuning with knowledge-guided context optimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6757–6767 (2023)
2023
Later among the works it cites.