HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation

arXiv:2507.00028v2 Announce Type: replace Abstract: The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns. Despite considerable progress, the challenge of designing trajectory representations that can capture diverse and complementary information remains an open research problem. Existing methods struggle in incorporating trajectory fine-grained details and high-level summary in a single model, limiting their ability to attend to both long-term dependencies while preserving local nuances. To address this, we propose HiT-JEPA (Hiera

Source: arXiv cs.LG — read the full report at the original publisher.

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