Fetching the paper…

Reading the bibliography…

2022

PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map

Xu, Chenfeng, Li, Tian, Tang, Chen et al.

Original
PDF
Post on X

Understand

Deep learning has recently achieved significant progress in trajectory forecasting.

However, the scarcity of trajectory data inhibits the data-hungry deep-learning models from learning good representations.
While mature representation learning methods exist in computer vision and natural language processing, these pre-training methods require large-scale data.
It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e.g., 34K samples in the nuScenes dataset).

PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map · Around

Built on

Deng, L.: The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine 29
2012
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Battaglia, P.W., Hamrick, J.B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al.: Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)
Original
2018
Earlier work this paper cites.
Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv e-prints pp. arXiv–1807 (2018)
2018
Earlier work this paper cites.
Chai, Y., Sapp, B., Bansal, M., Anguelov, D.: Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. arXiv preprint arXiv:1910.05449 (2019)
Original
2019
Earlier work this paper cites.
Cui, H., Radosavljevic, V., Chou, F.C., Lin, T.H., Nguyen, T., Huang, T.K., Schneider, J., Djuric, N.: Multimodal trajectory predictions for autonomous driving using deep convolutional networks. In: 2019 International Conference on Robotics and Automation (ICRA). pp. 2090–2096. IEEE (2019)
2019
Earlier work this paper cites.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding (2019)
2019
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners (2020)
2020
Earlier work this paper cites.
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11621–11631 (2020)
2020
Earlier work this paper cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning. pp. 1597–1607. PMLR (2020)
2020
Earlier work this paper cites.
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.

Similar

Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)
Original
2020
Cited alongside, same era.
Gao, J., Sun, C., Zhao, H., Shen, Y., Anguelov, D., Li, C., Schmid, C.: Vectornet: Encoding hd maps and agent dynamics from vectorized representation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Cited alongside, same era.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9729–9738 (2020)
2020
Cited alongside, same era.
Liang, M., Yang, B., Hu, R., Chen, Y., Liao, R., Feng, S., Urtasun, R.: Learning lane graph representations for motion forecasting. In: European Conference on Computer Vision. pp. 541–556. Springer (2020)
2020
Cited alongside, same era.

Then

Gao, T., Yao, X., Chen, D.: Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821 (2021)
Original
2021
Later among the works it cites.
Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., Moutarde, F.: Gohome: Graph-oriented heatmap output for future motion estimation. arXiv preprint arXiv:2109.01827 (2021)
Original
2021
Later among the works it cites.
Gilles, T., Sabatini, S., Tsishkou, D., Stanciulescu, B., Moutarde, F.: Thomas: Trajectory heatmap output with learned multi-agent sampling. arXiv preprint arXiv:2110.06607 (2021)
Original
2021
Later among the works it cites.
Gu, J., Sun, Q., Zhao, H.: Densetnt: Waymo open dataset motion prediction challenge 1st place solution. arXiv preprint arXiv:2106.14160 (2021)
Original
2021
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Ma, Y.J., Priya Inala, J., Jayaraman, D., Bastani, O.: Diverse sampling for normalizing flow based trajectory forecasting. arXiv e-prints pp. arXiv–2011 (2020)

2020

Cited alongside, same era.

Phan-Minh, T., Grigore, E.C., Boulton, F.A., Beijbom, O., Wolff, E.M.: Covernet: Multimodal behavior prediction using trajectory sets. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14074–14083 (2020)

2020

Cited alongside, same era.

Phan-Minh, T., Grigore, E.C., Boulton, F.A., Beijbom, O., Wolff, E.M.: Covernet: Multimodal behavior prediction using trajectory sets. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

2020

Cited alongside, same era.

Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. In: European Conference on Computer Vision. pp. 683–700. Springer (2020)

2020

Cited alongside, same era.

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T.L., Gugger, S., Drame, M., Lhoest, Q., Rush, A.M.: Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. pp. 38–45. Association for Computational Linguistics, Online (Oct 2020), https://www.aclweb.org/anthology/2020.emnlp-demos.6

2020

Cited alongside, same era.

Yuan, Y., Kitani, K.: Dlow: Diversifying latent flows for diverse human motion prediction. In: European Conference on Computer Vision. pp. 346–364. Springer (2020)

2020

Cited alongside, same era.

Zhao, H., Gao, J., Lan, T., Sun, C., Sapp, B., Varadarajan, B., Shen, Y., Shen, Y., Chai, Y., Schmid, C., Li, C., Anguelov, D.: Tnt: Target-driven trajectory prediction (2020)

2020

Cited alongside, same era.

Li, J., Selvaraju, R.R., Gotmare, A.D., Joty, S., Xiong, C., Hoi, S.: Align before fuse: Vision and language representation learning with momentum distillation. In: NeurIPS (2021)

2021

Later among the works it cites.

Ma, H., Sun, Y., Li, J., Tomizuka, M.: Multi-agent driving behavior prediction across different scenarios with self-supervised domain knowledge. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (2021)

2021

Later among the works it cites.

Ngiam, J., Caine, B., Vasudevan, V., Zhang, Z., Chiang, H.L., Ling, J., Roelofs, R., Bewley, A., Liu, C., Venugopal, A., Weiss, D., Sapp, B., Chen, Z., Shlens, J.: Scene transformer: A unified multi-task model for behavior prediction and planning (2021)

2021

Later among the works it cites.

Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. pp. 8748–8763. PMLR (2021)

2021

Later among the works it cites.

Varadarajan, B., Hefny, A., Srivastava, A., Refaat, K.S., Nayakanti, N., Cornman, A., Chen, K., Douillard, B., Lam, C.P., Anguelov, D., et al.: Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction. arXiv preprint arXiv:2111.14973 (2021)

2021

Later among the works it cites.

Yuan, Y., Weng, X., Ou, Y., Kitani, K.: Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

2021

Later among the works it cites.

Deo, N., Wolff, E., Beijbom, O.: Multimodal trajectory prediction conditioned on lane-graph traversals. In: Conference on Robot Learning. pp. 203–212. PMLR (2022)

2022

Closest in time.