T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation
Authors: 
Lihuan Li, Hao Xue, Yang Song, Flora SalimAuthors Info & Claims
Pages 569 - 572
Abstract
Trajectory similarity computation is crucial for analyzing movement patterns in applications like traffic management and wildlife tracking. Recent self-supervised learning methods such as contrastive learning have made advancements in trajectory representation learning but rely on predefined data augmentation schemes, limiting generalized and robust high-level semantic understanding. We introduce T-JEPA, a self-supervised method using Joint-Embedding Predictive Architecture (JEPA) to enhance trajectory representation learning. By sampling and predicting in representation space, T-JEPA infers high-level trajectory semantics without manual intervention. Extensive experiments conducted on three urban and two Foursquare datasets verify the effectiveness of T-JEPA in trajectory similarity computation.
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Index Terms
- T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation
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Published In
October 2024
743 pages
ISBN:9798400711077
DOI:10.1145/3678717
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Published: 22 November 2024
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SIGSPATIAL '24
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SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems
October 29 - November 1, 2024
GA, Atlanta, USA
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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%
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