Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models

Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. pp. 249–256. JMLR Workshop and Conference Proceedings (2010)

2010

Earlier work this paper cites.

Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? NeurIPS 27

2014

Earlier work this paper cites.

Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR. pp. 2117–2125 (2017)

2017

Earlier work this paper cites.

Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

Original

2017

Earlier work this paper cites.

Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: CVPR. pp. 652–660 (2017)

2017

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. NeurIPS 30

2017

Earlier work this paper cites.

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

Original

2018

Earlier work this paper cites.

Chang, M.F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., et al.: Argoverse: 3d tracking and forecasting with rich maps. In: CVPR. pp. 8748–8757 (2019)

2019

Earlier work this paper cites.

Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for nlp. In: ICML. pp. 2790–2799. PMLR (2019)

2019

Earlier work this paper cites.

Jeong, Y., Yi, K.: Target vehicle motion prediction-based motion planning framework for autonomous driving in uncontrolled intersections. IEEE Transactions on Intelligent Transportation Systems 22

2019

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: CVPR. pp. 11621–11631 (2020)

2020

Earlier work this paper cites.

Cai, H., Gan, C., Zhu, L., Han, S.: Tinytl: Reduce memory, not parameters for efficient on-device learning. NeurIPS 33

2020

Earlier work this paper cites.

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: CVPR. pp. 11525–11533 (2020)

2020

Earlier work this paper cites.

Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., Gurevych, I.: Adapterfusion: Non-destructive task composition for transfer learning. arXiv preprint arXiv:2005.00247 (2020)

Original

2020

Earlier work this paper cites.

Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. arXiv preprint arXiv:2007.07779 (2020)

Original

2020

Earlier work this paper cites.

Zhang, J.O., Sax, A., Zamir, A., Guibas, L., Malik, J.: Side-tuning: a baseline for network adaptation via additive side networks. In: ECCV. pp. 698–714. Springer (2020)

2020

Earlier work this paper cites.

Girgis, R., Golemo, F., Codevilla, F., Weiss, M., D’Souza, J.A., Kahou, S.E., Heide, F., Pal, C.: Latent variable sequential set transformers for joint multi-agent motion prediction. arXiv preprint arXiv:2104.00563 (2021)

Original

2021

Cited alongside, same era.

Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)

Original

2021

Cited alongside, same era.

Liu, Y., Yan, Q., Alahi, A.: Social nce: Contrastive learning of socially-aware motion representations. In: ICCV. pp. 15118–15129 (2021)

2021

Cited alongside, same era.

Zaken, E.B., Ravfogel, S., Goldberg, Y.: Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. arXiv preprint arXiv:2106.10199 (2021)

Original

2021

Cited alongside, same era.

Cheng, J., Mei, X., Liu, M.: Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders. In: ICCV. pp. 8679–8689 (2023)

2023

Later among the works it cites.

Chib, P.S., Singh, P.: Enhancing trajectory prediction through self-supervised waypoint noise prediction. arXiv preprint arXiv:2312.09466 (2023)

Original

2023

Later among the works it cites.

Feng, C., Zhou, H., Lin, H., Zhang, Z., Xu, Z., Zhang, C., Zhou, B., Shen, S.: Macformer: Map-agent coupled transformer for real-time and robust trajectory prediction. IEEE Robotics and Automation Letters (2023)

2023

Later among the works it cites.

Gao, X., Jia, X., Li, Y., Xiong, H.: Dynamic scenario representation learning for motion forecasting with heterogeneous graph convolutional recurrent networks. IEEE Robotics and Automation Letters 8

2023

Later among the works it cites.