| Self-supervised visual feature learning with deep neural networks: A survey L Jing, Y Tian IEEE transactions on pattern analysis and machine intelligence 43 (11), 4037 …, 2020 | 2183 | 2020 |
| Self-supervised spatiotemporal feature learning via video rotation prediction L Jing, X Yang, J Liu, Y Tian arXiv preprint arXiv:1811.11387, 2018 | 235* | 2018 |
| Coarse-to-fine semantic segmentation from image-level labels L Jing, Y Chen, Y Tian IEEE transactions on image processing 29, 225-236, 2019 | 128 | 2019 |
| LGAN: Lung segmentation in CT scans using generative adversarial network J Tan, L Jing, Y Huo, L Li, O Akin, Y Tian Computerized Medical Imaging and Graphics 87, 101817, 2021 | 82 | 2021 |
| Learning from Temporal Gradient for Semi-supervised Action Recognition J Xiao, L Jing, L Zhang, J He, Q She, Z Zhou, A Yuille, Y Li Computer Vision and Pattern Recognition (CVPR), 2022 | 75 | 2022 |
| Videossl: Semi-supervised learning for video classification L Jing, T Parag, Z Wu, Y Tian, H Wang Proceedings of the IEEE/CVF winter conference on applications of computer …, 2021 | 74 | 2021 |
| Cross-modal center loss for 3D cross-modal retrieval L Jing, E Vahdani, J Tan, Y Tian Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 69 | 2021 |
| Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth Z Feng, L Yang, L Jing, H Wang, YL Tian, B Li ECCV 2022, 2022 | 56 | 2022 |
| Video you only look once: Overall temporal convolutions for action recognition L Jing, X Yang, Y Tian Journal of Visual Communication and Image Representation 52, 58-65, 2018 | 49 | 2018 |
| Self-supervised feature learning by cross-modality and cross-view correspondences L Jing, L Zhang, Y Tian Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 42 | 2021 |
| 3D convolutional neural network with multi-model framework for action recognition L Jing, Y Ye, X Yang, Y Tian 2017 IEEE international conference on image processing (ICIP), 1837-1841, 2017 | 32 | 2017 |
| Multimodal Semi-Supervised Learning for 3D Objects Z Chen, L Jing, Y Liang, YL Tian, B Li The British Machine Vision Conference, 2021 | 30 | 2021 |
| Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR Z Feng, L Jing, P Yin, Y Tian, B Li The Conference on Robot Learning (CoRL), 2021 | 30 | 2021 |
| Recognizing american sign language manual signs from rgb-d videos L Jing, E Vahdani, M Huenerfauth, Y Tian arXiv preprint arXiv:1906.02851, 2019 | 30 | 2019 |
| Multi-camera Vehicle Tracking and Re-identification on AI City Challenge 2019 Y Chen, L Jing, E Vahdani, L Zhang, M He, Y Tian Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 30 | 2019 |
| Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects L Jing, Z Chen, B Li, Y Tian | 29* | |
| Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models Z Chen, L Jing, Y Li, B Li Advances on Neural Information Processing Systems (NeurIPS), 2023, 2023 | 27 | 2023 |
| Class-Level Confidence Based 3D Semi-Supervised Learning Z Chen, L Jing, L Yang, Y Li, B Li IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, 2023 | 21 | 2023 |
| An isolated-signing RGBD dataset of 100 American Sign Language signs produced by fluent ASL signers S Hassan, L Berke, E Vahdani, L Jing, Y Tian, M Huenerfauth Proceedings of the LREC2020 9th workshop on the representation and …, 2020 | 19 | 2020 |
| Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking L Jing, R Yu, H Kretzschmar, K Li, CR Qi, H Zhao, A Ayvaci, X Chen, ... International Conference on Robotics and Automation (ICRA), 2022 | 18 | 2022 |
Yingli TianDistinguished Professor, EE of The City College and CS of the Graduate Center, CUNYVerified email at ccny.cuny.edu
