| Contrastive multiview coding Y Tian, D Krishnan, P Isola arXiv preprint arXiv:1906.05849, 2019 | 1072 | 2019 |
| Representation Learning on Graphs with Jumping Knowledge Networks K Xu, C Li, Y Tian, T Sonobe, K Kawarabayashi, S Jegelka International Conference on Machine Learning (ICML), 2018 | 965 | 2018 |
| Supervised contrastive learning P Khosla, P Teterwak, C Wang, A Sarna, Y Tian, P Isola, A Maschinot, ... NeurIPS 2020, 2020 | 963 | 2020 |
| Deep Learning Strong Parts for Pedestrian Detection Y Tian, P Luo, X Wang, X Tang Computer Vision (ICCV), 2015 IEEE International Conference on, 1904-1912, 2015 | 499 | 2015 |
| Deepid-net: Deformable deep convolutional neural networks for object detection W Ouyang, X Wang, X Zeng, S Qiu, P Luo, Y Tian, H Li, S Yang, Z Wang, ... Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 478 | 2015 |
| Pedestrian detection aided by deep learning semantic tasks Y Tian, P Luo, X Wang, X Tang Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, 2015 | 452 | 2015 |
| What makes for good views for contrastive learning Y Tian, C Sun, B Poole, D Krishnan, C Schmid, P Isola NeurIPS 2020, 2020 | 433 | 2020 |
| Contrastive representation distillation Y Tian, D Krishnan, P Isola International Conference on Learning Representations (ICLR), 2020 | 403 | 2020 |
| Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? Y Tian, Y Wang, D Krishnan, JB Tenenbaum, P Isola European Conference on Computer Vision (ECCV 2020), 2020 | 365 | 2020 |
| Through-wall human pose estimation using radio signals M Zhao, T Li, M Abu Alsheikh, Y Tian, H Zhao, A Torralba, D Katabi Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 364 | 2018 |
| Switchable deep network for pedestrian detection Y Tian*, P Luo*, X Wang, X Tang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 292 | 2014 |
| RF-based 3D skeletons M Zhao, Y Tian, H Zhao, MA Alsheikh, T Li, R Hristov, Z Kabelac, D Katabi, ... ACM SIGCOMM 2018, 267-281, 2018 | 187 | 2018 |
| Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection W Ouyang, P Luo, X Zeng, S Qiu, Y Tian, H Li, S Yang, Z Wang, Y Xiong, ... arXiv preprint arXiv:1409.3505, 2014 | 151 | 2014 |
| DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks W Ouyang, X Zeng, X Wang, S Qiu, P Luo, Y Tian, H Li, S Yang, Z Wang, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (7), 1320-1334, 2017 | 103 | 2017 |
| Learning to Infer and Execute 3D Shape Programs Y Tian, A Luo, X Sun, K Ellis, WT Freeman, JB Tenenbaum, J Wu International Conference on Learning Representations (ICLR), 2019 | 95 | 2019 |
| RF-based fall monitoring using convolutional neural networks Y Tian, GH Lee, H He, CY Hsu, D Katabi ACM UbiComp 2018 2 (3), 1-24, 2018 | 92 | 2018 |
| ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees H He, H Wang, GH Lee, Y Tian International Conference on Learning Representations (ICLR), 2019 | 29 | 2019 |
| Divide and Contrast: Self-supervised Learning from Uncurated Data Y Tian, OJ Henaff, A Oord International Conference on Computer Vision (ICCV), 2021 | 21 | 2021 |
| Training-free uncertainty estimation for neural networks L Mi, H Wang, Y Tian, N Shavit | 13 | 2019 |
| Generative models as a data source for multiview representation learning A Jahanian, X Puig, Y Tian, P Isola arXiv preprint arXiv:2106.05258, 2021 | 10 | 2021 |
Dilip KrishnanResearch Scientist, Google在 google.com 的电子邮件经过验证
