| Few-shot class-incremental learning X Tao, X Hong, X Chang, S Dong, X Wei, Y Gong Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 404 | 2020 |
| Transductive semi-supervised deep learning using min-max features W Shi, Y Gong, C Ding, ZMX Tao, N Zheng Proceedings of the European Conference on Computer Vision (ECCV), 299-315, 2018 | 259 | 2018 |
| Topology-preserving class-incremental learning X Tao, X Chang, X Hong, X Wei, Y Gong Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 172 | 2020 |
| Few-shot class-incremental learning via relation knowledge distillation S Dong, X Hong, X Tao, X Chang, X Wei, Y Gong Proceedings of the AAAI Conference on Artificial Intelligence 35 (2), 1255-1263, 2021 | 124 | 2021 |
| Constructing deep sparse coding network for image classification S Zhang, J Wang, X Tao, Y Gong, N Zheng Pattern Recognition 64, 130-140, 2017 | 88 | 2017 |
| Fine-grained image classification using modified DCNNs trained by cascaded softmax and generalized large-margin losses W Shi, Y Gong, X Tao, D Cheng, N Zheng IEEE transactions on neural networks and learning systems 30 (3), 683-694, 2018 | 58 | 2018 |
| Training DCNN by combining max-margin, max-correlation objectives, and correntropy loss for multilabel image classification W Shi, Y Gong, X Tao, N Zheng IEEE transactions on neural networks and learning systems 29 (7), 2896-2908, 2017 | 45 | 2017 |
| Improving CNN performance accuracies with min–max objective W Shi, Y Gong, X Tao, J Wang, N Zheng IEEE transactions on neural networks and learning systems 29 (7), 2872-2885, 2017 | 43 | 2017 |
| Model behavior preserving for class-incremental learning Y Liu, X Hong, X Tao, S Dong, J Shi, Y Gong IEEE Transactions on Neural Networks and Learning Systems 34 (10), 7529-7540, 2022 | 37 | 2022 |
| Bi-objective continual learning: Learning ‘new’while consolidating ‘known’ X Tao, X Hong, X Chang, Y Gong Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5989-5996, 2020 | 28 | 2020 |
| Object detection with class aware region proposal network and focused attention objective X Tao, Y Gong, W Shi, D Cheng Pattern Recognition Letters 130, 353-361, 2020 | 23 | 2020 |
| Consistency-Preserving deep hashing for fast person re-identification D Li, Y Gong, D Cheng, W Shi, X Tao, X Chang Pattern Recognition 94, 207-217, 2019 | 19 | 2019 |
| Entropy and orthogonality based deep discriminative feature learning for object recognition W Shi, Y Gong, D Cheng, X Tao, N Zheng Pattern Recognition 81, 71-80, 2018 | 17 | 2018 |
| Class incremental learning for video action classification J Ma, X Tao, J Ma, X Hong, Y Gong 2021 IEEE International Conference on Image Processing (ICIP), 504-508, 2021 | 9 | 2021 |
| Analogy-detail networks for object recognition X Tao, X Hong, W Shi, X Chang, Y Gong IEEE Transactions on Neural Networks and Learning Systems 32 (10), 4404-4418, 2020 | 7 | 2020 |
| Structural knowledge organization and transfer for class-incremental learning Y Liu, X Hong, X Tao, S Dong, J Shi, Y Gong Proceedings of the 3rd ACM International Conference on Multimedia in Asia, 1-7, 2021 | 4 | 2021 |
| Brain cognition-inspired dual-pathway CNN architecture for image classification S Dong, Y Gong, J Shi, M Shang, X Tao, X Wei, X Hong, T Zhou IEEE Transactions on Neural Networks and Learning Systems, 2023 | 3 | 2023 |
| Class-Incremental Learning with Topological Schemas of Memory Spaces X Chang, X Tao, X Hong, X Wei, W Ke, Y Gong 2020 25th International Conference on Pattern Recognition (ICPR), 9719-9726, 2021 | 2 | 2021 |
| A deep CNN with focused attention objective for integrated object recognition and localization X Tao, C Xu, Y Gong, J Wang Advances in Multimedia Information Processing-PCM 2016: 17th Pacific-Rim …, 2016 | 1 | 2016 |
| Supplementary Material for Few-Shot Class-Incremental Learning X Tao, X Hong, X Chang, S Dong, X Wei, Y Gong | | |
