Citation

Tsao, Li Wu and Wang, Yan Kai and Lin, Hao Siang and Shuai, Hong Han and Wong, Lai Kuan and Cheng, Wen Huang (2022) Social-SSL: Self-supervised Cross-Sequence Representation Learning Based on Transformers for Multi-agent Trajectory Prediction. In: European Conference on Computer Vision, 23–27 October 2022, Tel Aviv, Israel.

Full text not available from this repository.

Abstract

Earlier trajectory prediction approaches focus on ways of capturing sequential structures among pedestrians by using recurrent networks, which is known to have some limitations in capturing long sequence structures. To address this limitation, some recent works proposed Transformer-based architectures, which are built with attention mechanisms. However, these Transformer-based networks are trained end-to-end without capitalizing on the value of pre-training. In this work, we propose Social-SSL that captures cross-sequence trajectory structures via self-supervised pre-training, which plays a crucial role in improving both data efficiency and generalizability of Transformer networks for trajectory prediction. Specifically, Social-SSL models the interaction and motion patterns with three pretext tasks: interaction type prediction, closeness prediction, and masked cross-sequence to sequence pre-training. Comprehensive experiments show that Social-SSL outperforms the state-of-the-art methods by at least 12% and 20% on ETH/UCY and SDD datasets in terms of Average Displacement Error and Final Displacement Error (code available at https://github.com/Sigta678/Social-SSL.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Trajectory prediction, Self-supervised learning, Transformer, Representation learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	11 Jan 2023 01:59
Last Modified:	11 Jan 2023 01:59
URII:	http://shdl.mmu.edu.my/id/eprint/11059

Downloads

Edit (login required)