多模態預訓練利用多種數據類型(如文字、圖像、語音) 的資料關聯來增強模型在不同模態下的特徵表達能力，捕捉多模態間的相關性與交互作用。過去的研究未曾探討過文字與軌跡之間跨模態預訓練之相關議題，為了解決這項挑戰，本研究提出了一種創新的方法，稱作對比語言軌跡預訓練(Contrastive Language-Trajectory Pretraining)，旨在建立軌跡與文字兩個不同的模態之間的共同特徵空間，並設計兩種不同的模態對齊技術來提升整體效能，充分描述軌跡與文字之間關係。此外，本研究也創建一個新穎的排球三維軌跡與對應戰術描述之資料集，可用於排球戰術分析以及各種排球研究應用上。實驗結果顯示對比語言軌跡預訓練在軌跡與文字檢索的任務上具有優異的性能，且能應用在GPS 軌跡分類任務上，其效能優於先前的 GPS 軌跡分類方法，展現本研究良好的泛化能力。

Multimodal pretraining leverages diverse data types such as text, images, and audio to enhance model in extracting effective representations across different modalities. Previous studies have not explored cross-modal pretraining between text and trajectory data. To address this gap, this research introduces an innovative approach called Contrastive Language-Trajectory Pretraining. It aims to establish a shared feature space between trajectory and text modalities. We propose two alignment techniques to train the proposed framework and thus enhance performance of downstream tasks. Additionally, a novel dataset comprising threedimensional volleyball trajectories and corresponding tactical descriptions is created for volleyball tactical analysis and various volleyball applications. Experimental results demonstrate the effectiveness of Contrastive Language-Trajectory Pretraining in trajectory-to-text retrieval and text-to-trajectory retrieval tasks. We also empoly the proposed framework in GPS trajectory classification. The proposed method outperforms existing ones and showcases strong generalization capabilities.

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