[1] Wang Y, Liu L B. Bilinear residual attention networks for fine-grained image classification[J]. Laser & Optoelectronics Progress, 57, 121011(2020).

[2] Li S Y, Liu Y H, Zhang R F. Fine-grained image classification based on multi-scale feature fusion[J]. Laser & Optoelectronics Progress, 57, 121002(2020).

[3] Gao P, Wang W J, Zhuo K Q et al. Quantitative phase contrast microscopy based on convolutional neural networks(invited)[J]. Laser & Optoelectronics Progress, 61, 0211011(2024).

[4] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth[EB/OL], 16-16. https://arxiv.org/abs/2010.11929

[5] Zhao S, Wei G H, Zhao W H et al. Assistant diagnosis of pediatric pneumonia based on Vision Transformer[J]. Laser & Optoelectronics Progress, 60, 0410013(2023).

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

[7] Yang K, Lu X Q. Remote sensing scene classification based on local selection Vision Transformer[J]. Laser & Optoelectronics Progress, 60, 2228005(2023).

[8] Chen D J, Yu W J, Gao Y B. Lidar 3D target detection based on improved PointPillars[J]. Laser & Optoelectronics Progress, 60, 1028012(2023).

[9] Zhou L J, Mao J N. Vision Transformer-based recognition tasks: a critical review[J]. Journal of Image and Graphics, 28, 2969-3003(2023).

[10] Masana M, Liu X L, Twardowski B et al. Class-incremental learning: survey and performance evaluation on image classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 5513-5533(2023).

[11] McCloskey M, Cohen N J. Catastrophic interference in connectionist networks: the sequential learning problem[M]. Psychology of learning and motivation, 109-165(1989).

[12] van de Ven G M, Tuytelaars T, Tolias A S. Three types of incremental learning[J]. Nature Machine Intelligence, 4, 1185-1197(2022).

[13] Rebuffi S A, Kolesnikov A, Sperl G et al. iCaRL: incremental classifier and representation learning[C], 5533-5542(2017).

[14] Lopez-Paz D, Ranzato M A. Gradient episodic memory for continual learning[C](2017).

[15] Chaudhry A, Ranzato M, Rohrbach M et al. Efficient lifelong learning with A-GEM[EB/OL]. https://arxiv.org/abs/1812.00420

[16] Li Z Z, Hoiem D. Learning without forgetting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 2935-2947(2018).

[17] Kirkpatrick J, Pascanu R, Rabinowitz N et al. Overcoming catastrophic forgetting in neural networks[J]. Proceedings of the National Academy of Science, 114, 3521-3526(2017).

[18] Mallya A, Lazebnik S. PackNet: adding multiple tasks to a single network by iterative pruning[C], 7765-7773(2018).

[19] Liu P F, Yuan W Z, Fu J L et al. Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing[J]. ACM Computing Surveys, 55, 195(2023).

[20] Radford A, Kim J W, Hallacy C. Learning transferable visual models from natural language supervision[C], 8748-8763(2021).

[21] Zhou K Y, Yang J K, Loy C C et al. Learning to prompt for vision-language models[J]. International Journal of Computer Vision, 130, 2337-2348(2022).

[22] Wang Z F, Zhang Z Z, Lee C Y et al. Learning to prompt for continual learning[C], 139-149(2022).

[23] Wah C, Branson S, Welinder P et al. The Caltech-UCSD birds-200-2011 dataset[R](2011).

[24] Wei L M, Zhao K, Wang N et al. Fine-grained fish disease image recognition algorithm model[J]. Laser & Optoelectronics Progress, 60, 1610005(2023).

[25] Zhou D W, Sun H L, Ning J Y et al. Continual learning with pre-trained models: a survey[EB/OL]. https://arxiv.org/abs/2010.11929

[26] Ebrahimi S, Meier F, Calandra R et al. Adversarial continual learning[M]. Computer vision-ECCV 2020, 12356, 386-402(2020).