D2
Computer Vision and Machine Learning

Li Jiang (Postdoc)

Dr. Li Jiang

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Standort
E1 4 - 612
Telefon
+49 681 9325 2139
Fax
+49 681 9325 2099

Personal Information

Publications

Shi, S., Jiang, L., Dai, D., & Schiele, B. (n.d.). Motion Transformer with Global Intention Localization and Local Movement Refinement. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). New Orleans, LO, USA.
(Accepted/in press)
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BibTeX
@inproceedings{Shi_Neurips22, TITLE = {Motion Transformer with Global Intention Localization and Local Movement Refinement}, AUTHOR = {Shi, Shaoshuai and Jiang, Li and Dai, Dengxin and Schiele, Bernt}, LANGUAGE = {eng}, YEAR = {2022}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)}, ADDRESS = {New Orleans, LO, USA}, }
Endnote
%0 Conference Proceedings %A Shi, Shaoshuai %A Jiang, Li %A Dai, Dengxin %A Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Motion Transformer with Global Intention Localization and Local Movement Refinement : %G eng %U http://hdl.handle.net/21.11116/0000-000C-1853-C %D 2022 %B 36th Conference on Neural Information Processing Systems %Z date of event: 2022-11-28 - 2022-12-09 %C New Orleans, LO, USA %B Advances in Neural Information Processing Systems 35 %U https://openreview.net/forum?id=9t-j3xDm7_Q
Shi, S., Jiang, L., Dai, D., & Schiele, B. (2022). MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction. Retrieved from https://arxiv.org/abs/2209.10033
(arXiv: 2209.10033)
Abstract
In this report, we present the 1st place solution for motion prediction track<br>in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer<br>framework for multimodal motion prediction, which introduces a small set of<br>novel motion query pairs for generating better multimodal future trajectories<br>by jointly performing the intention localization and iterative motion<br>refinement. A simple model ensemble strategy with non-maximum-suppression is<br>adopted to further boost the final performance. Our approach achieves the 1st<br>place on the motion prediction leaderboard of 2022 Waymo Open Dataset<br>Challenges, outperforming other methods with remarkable margins. Code will be<br>available at https://github.com/sshaoshuai/MTR.<br>
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BibTeX
@online{Shi2209.10033, TITLE = {{MTR}-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction}, AUTHOR = {Shi, Shaoshuai and Jiang, Li and Dai, Dengxin and Schiele, Bernt}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2209.10033}, EPRINT = {2209.10033}, EPRINTTYPE = {arXiv}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, ABSTRACT = {In this report, we present the 1st place solution for motion prediction track<br>in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer<br>framework for multimodal motion prediction, which introduces a small set of<br>novel motion query pairs for generating better multimodal future trajectories<br>by jointly performing the intention localization and iterative motion<br>refinement. A simple model ensemble strategy with non-maximum-suppression is<br>adopted to further boost the final performance. Our approach achieves the 1st<br>place on the motion prediction leaderboard of 2022 Waymo Open Dataset<br>Challenges, outperforming other methods with remarkable margins. Code will be<br>available at https://github.com/sshaoshuai/MTR.<br>}, }
Endnote
%0 Report %A Shi, Shaoshuai %A Jiang, Li %A Dai, Dengxin %A Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction : %G eng %U http://hdl.handle.net/21.11116/0000-000C-184C-5 %U https://arxiv.org/abs/2209.10033 %D 2022 %X In this report, we present the 1st place solution for motion prediction track<br>in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer<br>framework for multimodal motion prediction, which introduces a small set of<br>novel motion query pairs for generating better multimodal future trajectories<br>by jointly performing the intention localization and iterative motion<br>refinement. A simple model ensemble strategy with non-maximum-suppression is<br>adopted to further boost the final performance. Our approach achieves the 1st<br>place on the motion prediction leaderboard of 2022 Waymo Open Dataset<br>Challenges, outperforming other methods with remarkable margins. Code will be<br>available at https://github.com/sshaoshuai/MTR.<br> %K Computer Science, Computer Vision and Pattern Recognition, cs.CV
Shi, S., Jiang, L., Deng, J., Wang, Z., Guo, C., Shi, J., … Li, H. (2022). PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection. International Journal of Computer Vision. doi:10.1007/s11263-022-01710-9
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BibTeX
@article{Shi2022x, TITLE = {{PV-RCNN}++: {P}oint-Voxel Feature Set Abstraction With Local Vector Representation for {3D} Object Detection}, AUTHOR = {Shi, Shaoshuai and Jiang, Li and Deng, Jiajun and Wang, Zhe and Guo, Chaoxu and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng}, LANGUAGE = {eng}, ISSN = {0920-5691}, URL = {https://rdcu.be/c14JE}, DOI = {10.1007/s11263-022-01710-9}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, JOURNAL = {International Journal of Computer Vision}, }
Endnote
%0 Journal Article %A Shi, Shaoshuai %A Jiang, Li %A Deng, Jiajun %A Wang, Zhe %A Guo, Chaoxu %A Shi, Jianping %A Wang, Xiaogang %A Li, Hongsheng %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection : %G eng %U http://hdl.handle.net/21.11116/0000-000C-11CC-B %R 10.1007/s11263-022-01710-9 %U https://rdcu.be/c14JE %7 2022 %D 2022 %J International Journal of Computer Vision %O Int. J. Comput. Vis. %I Springer %C New York, NY %@ false
Lai, X., Liu, J., Jiang, L., Wang, L., Zhao, H., Liu, S., … Jia, J. (2022). Stratified Transformer for 3D Point Cloud Segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA: IEEE. doi:10.1109/CVPR52688.2022.00831
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BibTeX
@inproceedings{Lai_CVPR22, TITLE = {Stratified Transformer for {3D} Point Cloud Segmentation}, AUTHOR = {Lai, Xin and Liu, Jianhui and Jiang, Li and Wang, Liwei and Zhao, Hengshuang and Liu, Shu and Qi, Xiaojuan and Jia, Jiaya}, LANGUAGE = {eng}, ISBN = {978-1-6654-6946-3}, DOI = {10.1109/CVPR52688.2022.00831}, PUBLISHER = {IEEE}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)}, PAGES = {8490--8499}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Lai, Xin %A Liu, Jianhui %A Jiang, Li %A Wang, Liwei %A Zhao, Hengshuang %A Liu, Shu %A Qi, Xiaojuan %A Jia, Jiaya %+ External Organizations External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations %T Stratified Transformer for 3D Point Cloud Segmentation : %G eng %U http://hdl.handle.net/21.11116/0000-000C-13AB-E %R 10.1109/CVPR52688.2022.00831 %D 2022 %B 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition %Z date of event: 2022-06-19 - 2022-06-24 %C New Orleans, LA, USA %B IEEE/CVF Conference on Computer Vision and Pattern Recognition %P 8490 - 8499 %I IEEE %@ 978-1-6654-6946-3
Tian, Z., Lai, X., Jiang, L., Liu, S., Shu, M., Zhao, H., & Jia, J. (2022). Generalized Few-shot Semantic Segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA: IEEE. doi:10.1109/CVPR52688.2022.01127
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@inproceedings{Tian_CVPR22, TITLE = {Generalized Few-shot Semantic Segmentation}, AUTHOR = {Tian, Zhuotao and Lai, Xin and Jiang, Li and Liu, Shu and Shu, Michelle and Zhao, Hengshuang and Jia, Jiaya}, LANGUAGE = {eng}, ISBN = {978-1-6654-6946-3}, DOI = {10.1109/CVPR52688.2022.01127}, PUBLISHER = {IEEE}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)}, PAGES = {11553--11562}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Tian, Zhuotao %A Lai, Xin %A Jiang, Li %A Liu, Shu %A Shu, Michelle %A Zhao, Hengshuang %A Jia, Jiaya %+ External Organizations External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T Generalized Few-shot Semantic Segmentation : %G eng %U http://hdl.handle.net/21.11116/0000-000C-13E0-1 %R 10.1109/CVPR52688.2022.01127 %D 2022 %B 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition %Z date of event: 2022-06-19 - 2022-06-24 %C New Orleans, LA, USA %B IEEE/CVF Conference on Computer Vision and Pattern Recognition %P 11553 - 11562 %I IEEE %@ 978-1-6654-6946-3
Yang, Z., Jiang, L., Sun, Y., Schiele, B., & Jia, J. (2022). A Unified Query-based Paradigm for Point Cloud Understanding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA: IEEE. doi:10.1109/CVPR52688.2022.00835
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BibTeX
@inproceedings{Yang_CVPR22c, TITLE = {A Unified Query-based Paradigm for Point Cloud Understanding}, AUTHOR = {Yang, Zetong and Jiang, Li and Sun, Yanan and Schiele, Bernt and Jia, Jiaya}, LANGUAGE = {eng}, ISBN = {978-1-6654-6946-3}, DOI = {10.1109/CVPR52688.2022.00835}, PUBLISHER = {IEEE}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)}, PAGES = {8531--8541}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Yang, Zetong %A Jiang, Li %A Sun, Yanan %A Schiele, Bernt %A Jia, Jiaya %+ External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations %T A Unified Query-based Paradigm for Point Cloud Understanding : %G eng %U http://hdl.handle.net/21.11116/0000-000C-160E-D %R 10.1109/CVPR52688.2022.00835 %D 2022 %B 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition %Z date of event: 2022-06-19 - 2022-06-24 %C New Orleans, LA, USA %B IEEE/CVF Conference on Computer Vision and Pattern Recognition %P 8531 - 8541 %I IEEE %@ 978-1-6654-6946-3