{"ID":2840465,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13125","arxiv_id":"2511.13125","title":"Region-Point Joint Representation for Effective Trajectory Similarity Learning","abstract":"Recent learning-based methods have reduced the computational complexity of traditional trajectory similarity computation, but state-of-the-art (SOTA) methods still fail to leverage the comprehensive spectrum of trajectory information for similarity modeling. To tackle this problem, we propose \\textbf{RePo}, a novel method that jointly encodes \\textbf{Re}gion-wise and \\textbf{Po}int-wise features to capture both spatial context and fine-grained moving patterns. For region-wise representation, the GPS trajectories are first mapped to grid sequences, and spatial context are captured by structural features and semantic context enriched by visual features. For point-wise representation, three lightweight expert networks extract local, correlation, and continuous movement patterns from dense GPS sequences. Then, a router network adaptively fuses the learned point-wise features, which are subsequently combined with region-wise features using cross-attention to produce the final trajectory embedding. To train RePo, we adopt a contrastive loss with hard negative samples to provide similarity ranking supervision. Experiment results show that RePo achieves an average accuracy improvement of 22.2\\% over SOTA baselines across all evaluation metrics.","short_abstract":"Recent learning-based methods have reduced the computational complexity of traditional trajectory similarity computation, but state-of-the-art (SOTA) methods still fail to leverage the comprehensive spectrum of trajectory information for similarity modeling. To tackle this problem, we propose \\textbf{RePo}, a novel met...","url_abs":"https://arxiv.org/abs/2511.13125","url_pdf":"https://arxiv.org/pdf/2511.13125v1","authors":"[\"Hao Long\",\"Silin Zhou\",\"Lisi Chen\",\"Shuo Shang\"]","published":"2025-11-17T08:28:18Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.IR\",\"cs.LG\"]","methods":"[]","has_code":false}