[1] [1] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.https://doi.org/10.1109/CVPR.2016.90.

[2] [2] Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 4171–4186.https://doi.org/10.18653/v1/N19-1423.

[5] [5] Tan J R, Wang C B, Li B Y, et al. Equalization loss for long-tailed object recognition[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11659–11668.https://doi.org/10.1109/CVPR42600.2020.01168.

[6] [6] Li F F, Fergus R, Perona P. A Bayesian approach to unsupervised one-shot learning of object categories[C]//Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003: 1134−1141.https://doi.org/10.1109/ICCV.2003.1238476.

[7] [7] Mehrotra A, Dukkipati A. Generative adversarial residual pairwise networks for one shot learning[Z]. arXiv: 1703.08033, 2017.https://doi.org/10.48550/arXiv.1703.08033.

[9] [9] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//Proceedings of the 34th International Conference on Machine Learning, 2017: 1126–1135.https://doi.org/10.5555/3305381.3305498.

[10] [10] Rusu A A, Rao D, Sygnowski J, et al. Meta-learning with latent embedding optimization[C]//Proceedings of the 7th International Conference on Learning Representations, 2019.

[12] [12] Vinyals O, Blundell C, Lillicrap T, et al. Matching networks for one shot learning[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems, 2016: 3637–3645.https://doi.org/10.5555/3157382.3157504.

[13] [13] Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning[C]//Proceedings of the 31st Conference on Neural Information Processing Systems, 2017: 4077–4087.

[14] [14] Li W B, Wang L, Huo J L, et al. Revisiting local descriptor based image-to-class measure for few-shot learning[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 7253−7260.https://doi.org/10.1109/CVPR.2019.00743.

[15] [15] Luo X, Wei L H, Wen L J, et al. Rectifying the shortcut learning of background for few-shot learning[C]//Proceedings of the 35th Conference on Neural Information Processing Systems, 2021.

[16] [16] Chen Y H, Li W, Sakaridis C, et al. Domain adaptive faster R-CNN for object detection in the wild[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 3339−3348.https://doi.org/10.1109/CVPR.2018.00352.

[17] [17] Gong R, Li W, Chen Y H, et al. DLOW: domain flow for adaptation and generalization[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 2472–2481.https://doi.org/10.1109/CVPR.2019.00258.

[18] [18] He K M, Chen X L, Xie S N, et al. Masked autoencoders are scalable vision learners[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 15979–15988.https://doi.org/10.1109/CVPR52688.2022.01553.

[19] [19] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000–6010.https://doi.org/10.5555/3295222.3295349.

[20] [20] Ren M Y, Triantafillou E, Ravi S, et al. Meta-learning for semi-supervised few-shot classification[C]//Proceedings of the 6th International Conference on Learning Representations, 2018.

[21] [21] Sung F, Yang Y X, Zhang L, et al. Learning to compare: relation network for few-shot learning[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 1199–1208.https://doi.org/10.1109/CVPR.2018.00131.

[22] [22] Mishra N, Rohaninejad M, Chen X, et al. A simple neural attentive meta-learner[C]//Proceedings of the 6th International Conference on Learning Representations, 2018.

[23] [23] Simon C, Koniusz P, Nock R, et al. Adaptive subspaces for few-shot learning[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 4135–4144.https://doi.org/10.1109/CVPR42600.2020.00419.

[24] [24] Li H Y, Eigen D, Dodge S, et al. Finding task-relevant features for few-shot learning by category traversal[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 1–10.https://doi.org/10.1109/CVPR.2019.00009.

[25] [25] Oh J, Yoo H, Kim C H, et al. BOIL: towards representation change for few-shot learning[C]//Proceedings of the 9th International Conference on Learning Representations, 2021.

[26] [26] Lee K, Maji S, Ravichandran A, et al. Meta-learning with differentiable convex optimization[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 10649–10657.https://doi.org/10.1109/CVPR.2019.01091.

[27] [27] Liu Y B, Lee J, Park M, et al. Learning to propagate labels: Transductive propagation network for few-shot learning[C]//Proceedings of the 7th International Conference on Learning Representations, 2019.

[28] [28] Chen W Y, Liu Y C, Kira Z, et al. A closer look at few-shot classification[C]//Proceedings of the 7th International Conference on Learning Representations, 2019.