AINeutralarXiv – CS AI · Jun 255/10
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HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation
Researchers introduce HiT-JEPA, a hierarchical self-supervised learning framework that represents urban trajectory data across multiple semantic levels to improve similarity computation. The model captures fine-grained movement details, intermediate patterns, and high-level abstractions simultaneously, addressing limitations in existing approaches that struggle to balance local nuances with global dependencies.