Li Jiang (Postdoc)

Personal Information

Publications

2024

Conference paper

A. Das, X. Hu, L. Jiang, and B. Schiele

“MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment,” in Computer Vision -- ECCV 2024, Milano, Italy.

@inproceedings{DasECCV24,
TITLE = {{MTA-CLIP}: {L}anguage-Guided Semantic Segmentation with Mask-Text Alignment},
AUTHOR = {Das, Anurag and Hu, Xinting and Jiang, Li and Schiele, Bernt},
LANGUAGE = {eng},
PUBLISHER = {Springer},
YEAR = {2024},
PUBLREMARK = {Accepted},
BOOKTITLE = {Computer Vision -- ECCV 2024},
SERIES = {Lecture Notes in Computer Science},
ADDRESS = {Milano, Italy},
}

Endnote

%0 Conference Proceedings
%A Das, Anurag
%A Hu, Xinting
%A Jiang, Li
%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 MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-D869-8
%D 2024
%B 18th European Conference on Computer Vision 
%Z date of event: 2024-09-29 - 2024-10-04
%C Milano, Italy
%B Computer Vision -- ECCV 2024
%I Springer
%B Lecture Notes in Computer Science

Conference paper

H. Wang, H. Tang, L. Jiang, S. Shi, M. F. Naeem, H. Li, B. Schiele, and L. Wang

“GiT: Towards Generalist Vision Transformer through Universal Language Interface,” in Computer Vision -- ECCV 2024, Milano, Italy.

@inproceedings{WangECCV24,
TITLE = {{GiT}: {T}owards Generalist Vision Transformer through Universal Language Interface},
AUTHOR = {Wang, Haiyang and Tang, Hao and Jiang, Li and Shi, Shaoshuai and Naeem, Muhammad Ferjad and Li, Hongsheng and Schiele, Bernt and Wang, Liwei},
LANGUAGE = {eng},
PUBLISHER = {Springer},
YEAR = {2024},
PUBLREMARK = {Accepted},
BOOKTITLE = {Computer Vision -- ECCV 2024},
SERIES = {Lecture Notes in Computer Science},
ADDRESS = {Milano, Italy},
}

Endnote

%0 Conference Proceedings
%A Wang, Haiyang
%A Tang, Hao
%A Jiang, Li
%A Shi, Shaoshuai
%A Naeem, Muhammad Ferjad
%A Li, Hongsheng
%A Schiele, Bernt
%A Wang, Liwei
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
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
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
%T GiT: Towards Generalist Vision Transformer through Universal Language Interface : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-D87B-4
%D 2024
%B 18th European Conference on Computer Vision 
%Z date of event: 2024-09-29 - 2024-10-04
%C Milano, Italy
%B Computer Vision -- ECCV 2024
%I Springer
%B Lecture Notes in Computer Science

Conference paper

X. Wu, L. Jiang, P.-S. Wang, Z. Liu, X. Liu, Y. Qiao, W. Ouyang, T. He, and H. Zhao

“Point Transformer V3: Simpler, Faster, Stronger,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), Seattle, WA, USA.

@inproceedings{Wu_CVPR2024,
TITLE = {Point Transformer V3: Simpler, Faster, Stronger},
AUTHOR = {Wu, Xiaoyang and Jiang, Li and Wang, Peng-Shuai and Liu, Zhijian and Liu, Xihui and Qiao, Yu and Ouyang, Wanli and He, Tong and Zhao, Hengshuang},
LANGUAGE = {eng},
PUBLISHER = {IEEE},
YEAR = {2024},
PUBLREMARK = {Accepted},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)},
ADDRESS = {Seattle, WA, USA},
}

Endnote

%0 Conference Proceedings
%A Wu, Xiaoyang
%A Jiang, Li
%A Wang, Peng-Shuai
%A Liu, Zhijian
%A Liu, Xihui
%A Qiao, Yu
%A Ouyang, Wanli
%A He, Tong
%A Zhao, Hengshuang
%+ External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
External Organizations
External Organizations
External Organizations
External Organizations
External Organizations
External Organizations
%T Point Transformer V3: Simpler, Faster, Stronger : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000E-F560-1
%D 2024
%B 37th IEEE/CVF Conference on Computer Vision and Pattern Recognition
%Z date of event: 2024-06-17 - 2024-06-21
%C Seattle, WA, USA
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%I IEEE

2023

Conference paper

D2D6

L. Jiang, Z. Yang, S. Shi, V. Golyanik, D. Dai, and B. Schiele

“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, 2023.

@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},
ISBN = {979-8-3503-0129-8},
DOI = {10.1109/CVPR52729.2023.00119},
PUBLISHER = {IEEE},
YEAR = {2023},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)},
PAGES = {1168--1178},
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
%R 10.1109/CVPR52729.2023.00119
%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
%P 1168 - 1178
%I IEEE
%@ 979-8-3503-0129-8

Conference paper

Z. Tian, J. Cui, L. Jiang, X. Qi, X. Lai, Y. Chen, S. Liu, and J. Jia

“Learning Context-Aware Classifier for Semantic Segmentation,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, USA, 2023.

@inproceedings{Tian_AAAI23,
TITLE = {Learning Context-Aware Classifier for Semantic Segmentation},
AUTHOR = {Tian, Zhuotao and Cui, Jiequan and Jiang, Li and Qi, Xiaojuan and Lai, Xin and Chen, Yixin and Liu, Shu and Jia, Jiaya},
LANGUAGE = {eng},
ISBN = {978-1-57735-880-0},
DOI = {10.1609/aaai.v37i2.25340},
PUBLISHER = {AAAI},
YEAR = {2023},
MARGINALMARK = {$\bullet$},
DATE = {2023},
BOOKTITLE = {Proceedings of the 37th AAAI Conference on Artificial Intelligence},
PAGES = {2438--2446},
ADDRESS = {Washington, DC, USA},
}

Endnote

%0 Conference Proceedings
%A Tian, Zhuotao
%A Cui, Jiequan
%A Jiang, Li
%A Qi, Xiaojuan
%A Lai, Xin
%A Chen, Yixin
%A Liu, Shu
%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 Learning Context-Aware Classifier for Semantic Segmentation : 
%G eng
%U http://hdl.handle.net/21.11116/0000-000F-BB5B-9
%R 10.1609/aaai.v37i2.25340 
%D 2023
%B 37th AAAI Conference on Artificial Intelligence
%Z date of event: 2023-02-07 - 2023-02-14
%C Washington, DC, USA
%B Proceedings of the 37th AAAI Conference on Artificial Intelligence
%P 2438 - 2446
%I AAAI
%@ 978-1-57735-880-0
%U https://ojs.aaai.org/index.php/AAAI/article/view/25340/25112

2022

Conference paper

S. Shi, L. Jiang, D. Dai, and B. Schiele

“Motion Transformer with Global Intention Localization and Local Movement Refinement,” in Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, LA, USA, 2022.

@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

Conference paper

R. Ding, J. Yang, L. Jiang, and X. Qi

“DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation,” in Computer Vision -- ECCV 2022, Tel Aviv, Israel, 2022.

@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; Ciss&#233;, 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

Conference paper

X. Lai, J. Liu, L. Jiang, L. Wang, H. Zhao, S. Liu, X. Qi, and J. Jia

“Stratified Transformer for 3D Point Cloud Segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), New Orleans, LA, USA, 2022.

@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

Conference paper

Z. Tian, X. Lai, L. Jiang, S. Liu, M. Shu, H. Zhao, and J. Jia

“Generalized Few-shot Semantic Segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), New Orleans, LA, USA, 2022.

@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

Conference paper

Z. Yang, L. Jiang, Y. Sun, B. Schiele, and J. Jia

“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, 2022.

@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

Article

S. Shi, L. Jiang, J. Deng, Z. Wang, C. Guo, J. Shi, X. Wang, and H. Li

“PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection,” International Journal of Computer Vision, vol. 131, 2022.

@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

Paper

S. Shi, L. Jiang, D. Dai, and B. Schiele

“MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction,” 2022. [Online]. Available: https://arxiv.org/abs/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 github.com/sshaoshuai/MTR.

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