| Return of frustratingly easy domain adaptation B Sun, J Feng, K Saenko Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 1309 | 2016 |
| Dual path networks Y Chen, J Li, H Xiao, X Jin, S Yan, J Feng Advances in neural information processing systems 30, 2017 | 735 | 2017 |
| Scale-aware Fast R-CNN for Pedestrian Detection J Li, X Liang, SM Shen, T Xu, J Feng, S Yan https://arxiv.org/abs/1510.08160, 2016 | 683 | 2016 |
| Deep joint rain detection and removal from a single image W Yang, RT Tan, J Feng, J Liu, Z Guo, S Yan Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 638 | 2017 |
| Object region mining with adversarial erasing: A simple classification to semantic segmentation approach Y Wei, J Feng, X Liang, MM Cheng, Y Zhao, S Yan Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 609 | 2017 |
| End-to-end comparative attention networks for person re-identification H Liu, J Feng, M Qi, J Jiang, S Yan IEEE Transactions on Image Processing 26 (7), 3492-3506, 2017 | 566 | 2017 |
| Perceptual generative adversarial networks for small object detection J Li, X Liang, Y Wei, T Xu, J Feng, S Yan Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 552 | 2017 |
| A simple pooling-based design for real-time salient object detection JJ Liu, Q Hou, MM Cheng, J Feng, J Jiang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 529 | 2019 |
| Tokens-to-token vit: Training vision transformers from scratch on imagenet L Yuan, Y Chen, T Wang, W Yu, Y Shi, ZH Jiang, FEH Tay, J Feng, S Yan Proceedings of the IEEE/CVF International Conference on Computer Vision, 558-567, 2021 | 525 | 2021 |
| Natural language object retrieval R Hu, H Xu, M Rohrbach, J Feng, K Saenko, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 511 | 2016 |
| Stc: A simple to complex framework for weakly-supervised semantic segmentation Y Wei, X Liang, Y Chen, X Shen, MM Cheng, J Feng, Y Zhao, S Yan IEEE transactions on pattern analysis and machine intelligence 39 (11), 2314 …, 2016 | 485 | 2016 |
| Decoupling representation and classifier for long-tailed recognition B Kang, S Xie, M Rohrbach, Z Yan, A Gordo, J Feng, Y Kalantidis arXiv preprint arXiv:1910.09217, 2019 | 420 | 2019 |
| Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation Y Wei, H Xiao, H Shi, Z Jie, J Feng, TS Huang Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 418 | 2018 |
| Adversarial complementary learning for weakly supervised object localization X Zhang, Y Wei, J Feng, Y Yang, TS Huang Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 412 | 2018 |
| Tensor robust principal component analysis with a new tensor nuclear norm C Lu, J Feng, Y Chen, W Liu, Z Lin, S Yan IEEE transactions on pattern analysis and machine intelligence 42 (4), 925-938, 2019 | 396 | 2019 |
| Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization C Lu, J Feng, Y Chen, W Liu, Z Lin, S Yan Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 394 | 2016 |
| A^ 2-nets: Double attention networks Y Chen, Y Kalantidis, J Li, S Yan, J Feng Advances in neural information processing systems 31, 2018 | 380 | 2018 |
| Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution Y Chen, H Fan, B Xu, Z Yan, Y Kalantidis, M Rohrbach, S Yan, J Feng Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 374 | 2019 |
| Diversified visual attention networks for fine-grained object classification B Zhao, X Wu, J Feng, Q Peng, S Yan IEEE Transactions on Multimedia 19 (6), 1245-1256, 2017 | 360 | 2017 |
| Few-shot object detection via feature reweighting B Kang, Z Liu, X Wang, F Yu, J Feng, T Darrell Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 358 | 2019 |
Shuicheng Yan, Fellow of AAAI, ACM,...Sea AI Lab; previously NUS, Singapore在 sea.com 的電子郵件地址已通過驗證
