Learnable graph matching: Incorporating graph partitioning with deep feature learning for multiple object tracking J He, Z Huang, N Wang, Z Zhang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 107 | 2021 |
Densely Constrained Depth Estimator for Monocular 3D Object Detection Y Li, Y Chen, J He, Z Zhang European Conference on Computer Vision, 718-734, 2022 | 25 | 2022 |
3d video object detection with learnable object-centric global optimization J He, Y Chen, N Wang, Z Zhang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 8 | 2023 |
Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving Y Wang, J He, L Fan, H Li, Y Chen, Z Zhang arXiv preprint arXiv:2311.17918, 2023 | 7 | 2023 |
Tracking Objects with 3D Representation from Videos J He, L Fan, Y Wang, Y Chen, Z Huang, N Wang, Z Zhang arXiv preprint arXiv:2306.05416, 2023 | 1 | 2023 |
Scene-guided region proposal re-ranking method for on-road vehicle candidate generation Z Nan, Y Feng, J He, P Wei, L Xu, H Sun, N Zheng 2019 IEEE Intelligent Vehicles Symposium (IV), 2377-2382, 2019 | 1 | 2019 |
Learnable Graph Matching: A Practical Paradigm for Data Association J He, Z Huang, N Wang, Z Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | | 2024 |
Learning Pseudo 3D Representation for Ego-centric 2D Multiple Object Tracking J He, L Fan, Y Chen, Z Zhang | | 2023 |
2D Supervised Monocular 3D Object Detection by Global-to-Local 3D Reconstruction J He, Y Wang, Y Chen, Z Zhang arXiv preprint arXiv:2306.05418, 2023 | | 2023 |
Training method for multi-object tracking model and multi-object tracking method J He, Z Huang, N Wang US Patent App. 17/649,511, 2022 | | 2022 |
Video-based 3D Object Detection with Learnable Object-Centric Global Optimization J He, Y Chen, N Wang, Z Zhang | | 2022 |
Densely Constrained Depth Estimator for Monocular 3D Object Detection Supplementary Material Y Li, Y Chen, J He, Z Zhang | | |
3D Video Object Detection with Learnable Object-Centric Global Optimization Supplementary Material J He, Y Chen, N Wang, Z Zhang | | |