The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Multimodal Motion Prediction with Stacked Transformers
Open accessAbstract
Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve such multimodal motion prediction by implicitly regularizing the feature or explicitly generating multiple candidate proposals. However, it remains challenging since the latent features may concentrate on the most frequent mode of the data while the proposal-based methods depend largely on the prior knowledge to generate and select the proposals. In this work, we propose a novel transformer framework for multimodal motion prediction, termed as mmTransformer. A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals. A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on motion prediction, substantially improving the diversity and the accuracy of the predicted trajectories. Demo video and code are available at https://decisionforce.github.io/mmTransformer.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
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Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
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Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072
Query-Centric Trajectory Prediction
Zhou, Z., Wang, J., Li, Y., & Huang, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01713 · Cited by 333
Scene Transformer: A unified architecture for predicting multiple agent trajectories
Ngiam, J., Caine, B., Vasudevan, V., Zhang, Z., Chiang, H.-T. L., Ling, J., Roelofs, R., Bewley, A., Liu, C., Venugopal, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.08417 · Cited by 84
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
Malinin, A., Band, N., Ganshin, A., Chesnokov, G., Gal, Y., Gales, M. J. F., Noskov, A., Ploskonosov, A., Prokhorenkova, L., Provilkov, I., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.07455 · Cited by 48
Metadata
- Authors
- Liu, Y., Zhang, J., Fang, L., Jiang, Q., & Zhou, B.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 7573-7582
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3169575318
- Cited by
- 474
- References
- 35
- FWCI
- 79.64
- Type
- conference paper
- Language
- en
- Open access
- green
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