[1] Yang H, Li J. Label contrastive learning for image classification[J]. Soft Comput, 27, 13477-13486(2023).

[2] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C], 1-9(2015). https://doi.org/10.1109/CVPR.2015.7298594

[3] Lecun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proc IEEE, 86, 2278-2324(1998).

[4] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Commun ACM, 60, 84-90(2017).

[5] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016). https://doi.org/10.1109/CVPR.2016.90

[6] Xu S J, Jing Y, Li H T et al. Progressive multi-granularity ResNet vehicle recognition network[J]. Opto-Electron Eng, 50, 230052(2023).

[7] Wang J, Yang Q P, Yang S Q et al. Dual-path processing network for high-resolution salient object detection[J]. Appl Intell, 52, 12034-12048(2022).

[8] Xue T, Hong Y. IX-ResNet: fragmented multi-scale feature fusion for image classification[J]. Multimed Tools Appl, 80, 27855-27865(2021).

[9] Jiang Z W, Ma Z J, Wang Y N et al. Aggregated decentralized down-sampling-based ResNet for smart healthcare systems[J]. Neural Comput Appl, 35, 14653-14665(2023).

[10] Luo Z B, Sun Z T, Zhou W L et al. Rethinking ResNets: improved stacking strategies with high-order schemes for image classification[J]. Complex Intell Syst, 8, 3395-3407(2022).

[11] Jafar A, Lee M. High-speed hyperparameter optimization for deep ResNet models in image recognition[J]. Cluster Comput, 26, 2605-2613(2023).

[12] Chen L, Zhang J L, Peng H et al. Few-shot image classification via multi-scale attention and domain adaptation[J]. Opto-Electron Eng, 50, 220232(2023).

[13] Liang L M, Jin J X, Feng Y et al. Retinal lesions graded algorithm that integrates coordinate perception and hybrid extraction[J]. Opto-Electron Eng, 51, 230276(2024).

[14] Ye Y C, Chen Y. Single image rain removal based on cross scale attention fusion[J]. Opto-Electron Eng, 50, 230191(2023).

[15] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018). https://doi.org/10.1109/CVPR.2018.00745

[16] Ying Y, Zhang N B, Shan P et al. PSigmoid: improving squeeze-and-excitation block with parametric sigmoid[J]. Appl Intell, 51, 7427-7439(2021).

[17] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021). https://doi.org/10.1109/CVPR46437.2021.01350

[18] Ji Q W, Yu B, Yang Z W et al. LAM: lightweight attention module[C], 485-497(2022). https://doi.org/10.1007/978-3-031-10986-7_39

[19] Zhong H M, Han T T, Xia W et al. Research on real-time teachers’ facial expression recognition based on YOLOv5 and attention mechanisms[J]. EURASIP J Adv Signal Process, 2023, 55(2023).

[20] Qi F, Wang Y L, Tang Z. Lightweight plant disease classification combining GrabCut algorithm, new coordinate attention, and channel pruning[J]. Neural Process Lett, 54, 5317-5331(2022).

[21] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 448-456(2015).

[22] Abdi M, Nahavandi S. Multi-residual networks: improving the speed and accuracy of residual networks[Z](2016). https://doi.org/10.48550/arXiv.1609.05672

[23] Ma C X, Wu J B, Si C Y et al. Scaling supervised local learning with augmented auxiliary networks[Z](2024). https://doi.org/10.48550/arXiv.2402.17318

[24] Wu X D, Gao S Q, Zhang Z Y et al. Auto-train-once: controller network guided automatic network pruning from scratch[Z](2024). https://doi.org/10.48550/arXiv.2403.14729

[25] Huang G, Liu Z, Van Der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017). https://doi.org/10.1109/CVPR.2017.243

[26] Lan H, Wang X H, Shen H et al. Couplformer: rethinking vision transformer with coupling attention[C], 6464-6473(2023). https://doi.org/10.1109/WACV56688.2023.00641

[27] Zhou C L, Zhang H, Zhou Z K et al. QKFormer: hierarchical spiking transformer using Q-K attention[Z](2024). https://doi.org/10.48550/arXiv.2403.16552

[28] Shin H, Choi D W. Teacher as a lenient expert: teacher-agnostic data-free knowledge distillation[C], 14991-14999(2024). https://doi.org/10.1609/aaai.v38i13.29420

[29] Tan M X, Le Q. EfficientNet: rethinking model scaling for convolutional neural networks[C], 6105-6114(2019).