| The devil is in the channels: Mutual-channel loss for fine-grained image classification D Chang, Y Ding, J Xie, AK Bhunia, X Li, Z Ma, M Wu, J Guo, YZ Song IEEE Transactions on Image Processing 29, 4683-4695, 2020 | 389 | 2020 |
| Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments X Li, X Yang, Z Ma, JH Xue Pattern Recognition, 109381, 2023 | 157* | 2023 |
| BSNet: Bi-similarity network for few-shot fine-grained image classification X Li, J Wu, Z Sun, Z Ma, J Cao, JH Xue IEEE Transactions on Image Processing 30, 1318-1331, 2020 | 155 | 2020 |
| Dual cross-entropy loss for small-sample fine-grained vehicle classification X Li, L Yu, D Chang, Z Ma, J Cao IEEE Transactions on Vehicular Technology 68 (5), 4204-4212, 2019 | 154 | 2019 |
| Fine-grained vehicle classification with channel max pooling modified CNNs Z Ma, D Chang, J Xie, Y Ding, S Wen, X Li, Z Si, J Guo IEEE Transactions on Vehicular Technology 68 (4), 3224-3233, 2019 | 150 | 2019 |
| A concise review of recent few-shot meta-learning methods X Li, Z Sun, JH Xue, Z Ma Neurocomputing 456, 463-468, 2021 | 146 | 2021 |
| Image-text dual neural network with decision strategy for small-sample image classification F Zhu, Z Ma, X Li, G Chen, JT Chien, JH Xue, J Guo Neurocomputing 328, 182-188, 2019 | 64 | 2019 |
| Softmax cross entropy loss with unbiased decision boundary for image classification J Cao, Z Su, L Yu, D Chang, X Li, Z Ma 2018 Chinese automation congress (CAC), 2028-2032, 2018 | 52 | 2018 |
| Large-margin regularized softmax cross-entropy loss X Li, D Chang, T Tian, J Cao IEEE access 7, 19572-19578, 2019 | 51 | 2019 |
| Learning calibrated class centers for few-shot classification by pair-wise similarity Y Guo, R Du, X Li, J Xie, Z Ma, Y Dong IEEE Transactions on Image Processing 31, 4543-4555, 2022 | 50 | 2022 |
| OSLNet: Deep small-sample classification with an orthogonal softmax layer X Li, D Chang, Z Ma, ZH Tan, JH Xue, J Cao, J Yu, J Guo IEEE Transactions on Image Processing 29, 6482-6495, 2020 | 43 | 2020 |
| Bi-directional feature reconstruction network for fine-grained few-shot image classification J Wu, D Chang, A Sain, X Li, Z Ma, J Cao, J Guo, YZ Song Proceedings of the AAAI conference on artificial intelligence 37 (3), 2821-2829, 2023 | 40 | 2023 |
| Locally-enriched cross-reconstruction for few-shot fine-grained image classification X Li, Q Song, J Wu, R Zhu, Z Ma, JH Xue IEEE Transactions on Circuits and Systems for Video Technology 33 (12), 7530 …, 2023 | 35 | 2023 |
| Amortized Bayesian prototype meta-learning: A new probabilistic meta-learning approach to few-shot image classification Z Sun, J Wu, X Li, W Yang, JH Xue International Conference on Artificial Intelligence and Statistics, 1414-1422, 2021 | 25 | 2021 |
| Supervised latent Dirichlet allocation with a mixture of sparse softmax X Li, Z Ma, P Peng, X Guo, F Huang, X Wang, J Guo Neurocomputing 312, 324-335, 2018 | 24 | 2018 |
| ReNAP: Relation network with adaptiveprototypical learning for few-shot classification X Li, Y Li, Y Zheng, R Zhu, Z Ma, JH Xue, J Cao Neurocomputing 520, 356-364, 2023 | 19 | 2023 |
| ReMarNet: Conjoint relation and margin learning for small-sample image classification X Li, L Yu, X Yang, Z Ma, JH Xue, J Cao, J Guo IEEE Transactions on Circuits and Systems for Video Technology 31 (4), 1569-1579, 2020 | 14 | 2020 |
| Deep InterBoost networks for small-sample image classification X Li, D Chang, Z Ma, ZH Tan, JH Xue, J Cao, J Guo Neurocomputing 456, 492-503, 2021 | 13 | 2021 |
| Self-reconstruction network for fine-grained few-shot classification X Li, Z Li, J Xie, X Yang, JH Xue, Z Ma Pattern Recognition 153, 110485, 2024 | 11 | 2024 |
| CC-loss: Channel correlation loss for image classification Z Song, D Chang, Z Ma, X Li, ZH Tan 2020 25th International Conference on Pattern Recognition (ICPR), 7601-7608, 2021 | 10 | 2021 |
Zhanyu MaBeijing University of Posts and TelecommunicationsZweryfikowany adres z bupt.edu.cn
