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
E-mail: Get email via email

Personal Information

Publications

Shi, S., Jiang, L., Dai, D., & Schiele, B. (2022a). Motion Transformer with Global Intention Localization and Local Movement Refinement. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). New Orleans, LA, USA: Curran Associates, Inc.

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@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}, PUBLISHER = {Curran Associates, Inc.}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)}, EDITOR = {Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.}, PAGES = {6531--6543}, ADDRESS = {New Orleans, LA, 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, LA, USA %B Advances in Neural Information Processing Systems 35 %E Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K.; Oh, A. %P 6531 - 6543 %I Curran Associates, Inc. %U https://openreview.net/forum?id=9t-j3xDm7_Q

Shi, S., Jiang, L., Dai, D., & Schiele, B. (2022b). 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 in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer framework for multimodal motion prediction, which introduces a small set of novel motion query pairs for generating better multimodal future trajectories by jointly performing the intention localization and iterative motion refinement. A simple model ensemble strategy with non-maximum-suppression is adopted to further boost the final performance. Our approach achieves the 1st place on the motion prediction leaderboard of 2022 Waymo Open Dataset Challenges, outperforming other methods with remarkable margins. Code will be available at https://github.com/sshaoshuai/MTR.

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@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 in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer framework for multimodal motion prediction, which introduces a small set of novel motion query pairs for generating better multimodal future trajectories by jointly performing the intention localization and iterative motion refinement. A simple model ensemble strategy with non-maximum-suppression is adopted to further boost the final performance. Our approach achieves the 1st place on the motion prediction leaderboard of 2022 Waymo Open Dataset Challenges, outperforming other methods with remarkable margins. Code will be available at https://github.com/sshaoshuai/MTR. }, }

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 in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer framework for multimodal motion prediction, which introduces a small set of novel motion query pairs for generating better multimodal future trajectories by jointly performing the intention localization and iterative motion refinement. A simple model ensemble strategy with non-maximum-suppression is adopted to further boost the final performance. Our approach achieves the 1st place on the motion prediction leaderboard of 2022 Waymo Open Dataset Challenges, outperforming other methods with remarkable margins. Code will be available at https://github.com/sshaoshuai/MTR. %K Computer Science, Computer Vision and Pattern Recognition, cs.CV

Ding, R., Yang, J., Jiang, L., & Qi, X. (2022). DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation. In Computer Vision -- ECCV 2022. Tel Aviv, Israel: Springer. doi:10.1007/978-3-031-19812-0_17

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@inproceedings{Ding_ECCV2022, TITLE = {{DODA}: {D}ata-Oriented Sim-to-Real Domain Adaptation for {3D} Semantic Segmentation}, AUTHOR = {Ding, Runyu and Yang, Jihan and Jiang, Li and Qi, Xiaojuan}, LANGUAGE = {eng}, ISBN = {978-3-031-19811-3}, DOI = {10.1007/978-3-031-19812-0_17}, PUBLISHER = {Springer}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, DATE = {2022}, BOOKTITLE = {Computer Vision -- ECCV 2022}, EDITOR = {Avidan, Shai and Brostow, Gabriel and Ciss{\'e}, Moustapha and Farinella, Giovanni Maria and Hassner, Tal}, PAGES = {284--303}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {13687}, ADDRESS = {Tel Aviv, Israel}, }

Endnote

%0 Conference Proceedings %A Ding, Runyu %A Yang, Jihan %A Jiang, Li %A Qi, Xiaojuan %+ External Organizations External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations %T DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation : %G eng %U http://hdl.handle.net/21.11116/0000-000C-957F-E %R 10.1007/978-3-031-19812-0_17 %D 2022 %B 17th European Conference on Computer Vision %Z date of event: 2022-10-23 - 2022-10-27 %C Tel Aviv, Israel %B Computer Vision -- ECCV 2022 %E Avidan, Shai; Brostow, Gabriel; Cissé, Moustapha; Farinella, Giovanni Maria; Hassner, Tal %P 284 - 303 %I Springer %@ 978-3-031-19811-3 %B Lecture Notes in Computer Science %N 13687 %U https://rdcu.be/c5AvW

Jiang, L., Yang, Z., Shi, S., Golyanik, V., Dai, D., & Schiele, B. (n.d.). Self-Supervised Pre-Training With Masked Shape Prediction for 3D Scene Understanding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Vancouver, Canada: IEEE.

(Accepted/in press)

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@inproceedings{Jiang_CVPR23, TITLE = {Self-Supervised Pre-Training With Masked Shape Prediction for {3D} Scene Understanding}, AUTHOR = {Jiang, Li and Yang, Zetong and Shi, Shaoshuai and Golyanik, Vladislav and Dai, Dengxin and Schiele, Bernt}, LANGUAGE = {eng}, PUBLISHER = {IEEE}, YEAR = {2023}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)}, ADDRESS = {Vancouver, Canada}, }

Endnote

%0 Conference Proceedings %A Jiang, Li %A Yang, Zetong %A Shi, Shaoshuai %A Golyanik, Vladislav %A Dai, Dengxin %A Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Visual Computing and Artificial Intelligence, 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 Self-Supervised Pre-Training With Masked Shape Prediction for 3D Scene Understanding : %G eng %U http://hdl.handle.net/21.11116/0000-000D-1F66-F %D 2023 %B 36th IEEE/CVF Conference on Computer Vision and Pattern Recognition %Z date of event: 2023-06-18 - 2023-06-23 %C Vancouver, Canada %B IEEE/CVF Conference on Computer Vision and Pattern Recognition %I IEEE

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, 131. doi:10.1007/s11263-022-01710-9

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@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}, VOLUME = {131}, PAGES = {531--551}, }

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. %V 131 %& 531 %P 531 - 551 %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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@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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@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