| 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 | 200 | 2020 |
| 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 | 102 | 2019 |
| 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 | 95 | 2019 |
| 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 | 59 | 2020 |
| A concise review of recent few-shot meta-learning methods X Li, Z Sun, JH Xue, Z Ma Neurocomputing 456, 463-468, 2021 | 58 | 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 | 52 | 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 | 30 | 2018 |
| 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 | 25 | 2020 |
| Large-margin regularized softmax cross-entropy loss X Li, D Chang, T Tian, J Cao IEEE access 7, 19572-19578, 2019 | 25 | 2019 |
| 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 | 19 | 2018 |
| 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 | 13 | 2021 |
| 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 | 10 | 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 | 9 | 2021 |
| Deep metric learning for few-shot image classification: A selective review X Li, X Yang, Z Ma, JH Xue arXiv preprint arXiv:2105.08149, 2021 | 9 | 2021 |
| 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 | 6 | 2022 |
| Multi-view supervised latent dirichlet allocation X LI, R LI, F FENG, J CAO, X WANG ACTA ELECTONICA SINICA 42 (10), 2040, 2014 | 4 | 2014 |
| TLRM: Task-level relation module for GNN-based few-shot learning Y Guo, Z Ma, X Li, Y Dong 2021 International Conference on Visual Communications and Image Processing …, 2021 | 3 | 2021 |
| Mixed attention mechanism for small-sample fine-grained image classification X Li, J Wu, D Chang, W Huang, Z Ma, J Cao 2019 Asia-Pacific Signal and Information Processing Association Annual …, 2019 | 3 | 2019 |
| Channel max pooling layer for fine-grained vehicle classification Z Ma, D Chang, X Li arXiv preprint arXiv:1902.11107, 2019 | 3 | 2019 |
| Image-text dual model for small-sample image classification F Zhu, X Li, Z Ma, G Chen, P Peng, X Guo, JT Chien, J Guo Computer Vision: Second CCF Chinese Conference, CCCV 2017, Tianjin, China …, 2017 | 3 | 2017 |
