TRAJCROSS: Trajecotry Cross-Modal Retrieval with Contrastive Learning

In this paper, we propose a new task namely trajectory cross-modal retrieval which achieves the cross-modal search between coordinate trajectories and images containing trajectories. Nevertheless, trajectory cross-modal retrieval is rather challenging in learning the representations of each modality and reduce the cross-domain discrepancy caused by the inconsistent data distribution at the same time. we proposes a cross-modal retrieval model TRAJCROSS based on multi-level representation for trajectory cross-modal retrieval. Specifically, TRAJCROSS extracts the location features and the shape information respectively for the represention of multi-modal data. we adopt a contrastive learning method to achieve semantic preservation among similar multi-modal data. Extensive experiments show that TRAJCROSS significantly outperforms state-of-the-art cross-modal retrieval methods.