[1] Chang D L, Tong Y J, Du R Y et al. An erudite fine-grained visual classification model[C], 7268-7277(2023).

[2] Li W B, Xu J L, Huo J et al. Distribution consistency based covariance metric networks for few-shot learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 8642-8649(2019).

[3] He J, Chen J N, Liu S et al. TransFG: a transformer architecture for fine-grained recognition[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 852-860(2022).

[4] Tian Z S, Liu L B. Fine-grained image classification model based on improved transformer[J]. Laser & Optoelectronics Progress, 60, 0210013(2023).

[5] Zhang Y. A fine-grained image classification and detection method based on convolutional neural network fused with attention mechanism[J]. Computational Intelligence and Neuroscience, 2022, 2974960(2022).

[6] Huang G, Zheng Y L, Liao K Y et al. Mutual attention diversity feature fusion network-relevant fine-grained classification[J]. Journal of Image and Graphics, 28, 2420-2431(2023).

[7] Jin C, Tong C Q. Remote sensing image classification method based on fusion of CNN and transformer[J]. Laser & Optoelectronics Progress, 60, 2028006(2023).

[8] Wang Y Q, Yao Q M, Kwok J T et al. Generalizing from a few examples: a survey on few-shot learning[J]. ACM Computing Surveys, 53, 1-34(2021).

[9] Zhang W L, Song W. Fine-grained image classification based on feature fusion and ensemble learning[J]. Laser & Optoelectronics Progress, 61, 2237010(2024).

[10] Valero-Mas J J, Gallego A J, Rico-Juan J R. An overview of ensemble and feature learning in few-shot image classification using Siamese networks[J]. Multimedia Tools and Applications, 83, 19929-19952(2024).

[11] Xu Z C, Guo B F, Wu W H et al. Multi-scale feature extraction method of hyperspectral image with attention mechanism[J]. Laser & Optoelectronics Progress, 61, 0437010(2024).

[12] Snell J, Swersky K, Zemel R S. Prototypical networks for few-shot learning[EB/OL]. https://arxiv.org/abs/1703.05175v2

[13] Sung F, Yang Y X, Zhang L et al. Learning to compare: relation network for few-shot learning[C], 1199-1208(2018).

[14] Zhu Y H, Liu C L, Jiang S Q. Multi-attention meta learning for few-shot fine-grained image recognition[EB/OL]. https://dl.acm.org/doi/10.5555/3491440.3491592

[15] Wertheimer D, Tang L M, Hariharan B. Few-shot classification with feature map reconstruction networks[C], 8008-8017(2021).

[16] Zhang C, Cai Y J, Lin G S et al. DeepEMD: few-shot image classification with differentiable earth mover’s distance and structured classifiers[C], 12200-12210(2020).

[17] Zeng Z W, Li L H, Zhao Z F et al. Improved fine-grained image classification in few-shot learning based on channel-spatial attention and grouped bilinear convolution[J/OL]. The Visual Computer, 1-13. https://link.springer.com/article/10.1007/s00371-024-03650-6#citeas

[18] Zhu H G, Gao Z, Wang J Y et al. Few-shot fine-grained image classification via multi-frequency neighborhood and double-cross modulation[J]. IEEE Transactions on Multimedia, 26, 10264-10278(2024).

[19] Xie J T, Long F, Lü J M et al. Joint distribution matters: deep Brownian distance covariance for few-shot classification[C], 7962-7971(2022).

[20] Wah C, Branson S, Welinder P et al. The caltech-UCSD birds-200-2011 dataset[EB/OL]. https://www.semanticscholar.org/paper/The-Caltech-UCSD-Birds-200-2011-Dataset-Wah-Branson/c069629a51f6c1c301eb20ed77bc6b586c24ce32

[21] Krause J, Stark M, Jia D et al. 3D object representations for fine-grained categorization[C], 554-561(2013).

[22] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks[EB/OL]. https://arxiv.org/abs/1703.03400v3

[23] Chen W Y, Liu Y C, Kira Z et al. A closer look at few-shot classification[EB/OL]. https://arxiv.org/abs/1904.04232

[24] Vinyals O, Blundell C, Lillicrap T et al. Matching networks for one shot learning[EB/OL]. https://arxiv.org/abs/1606.04080

[25] Li W B, Wang L, Huo J et al. Asymmetric distribution measure for few-shot learning[EB/OL]. https://arxiv.org/abs/2002.00153

[26] Wertheimer D, Hariharan B. Few-shot learning with localization in realistic settings[C], 6551-6560(2019).

[27] Ma Z X, Chen Z D, Zhao L J et al. Cross-layer and cross-sample feature optimization network for few-shot fine-grained image classification[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 38, 4136-4144(2024).

[28] Li W B, Wang L, Xu J L et al. Revisiting local descriptor based image-to-class measure for few-shot learning[C], 7253-7260(2019).

[29] Abdelaziz M, Zhang Z P. Multi-scale kronecker-product relation networks for few-shot learning[J]. Multimedia Tools and Applications, 81, 6703-6722(2022).

[30] Li X X, Wu J J, Sun Z et al. BSNet: bi-similarity network for few-shot fine-grained image classification[J]. IEEE Transactions on Image Processing, 30, 1318-1331(2021).

[31] Hao F S, He F X, Cheng J et al. Collect and select: semantic alignment metric learning for few-shot learning[C], 8459-8468(2019).

[32] Doersch C, Gupta A, Zisserman A. CrossTransformers: spatially-aware few-shot transfer[EB/OL]. https://arxiv.org/abs/2007.11498v5

[33] Selvaraju R R, Cogswell M, Das A et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C], 618-626(2017).