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paperarXivTrust 82 · PrimaryPublished 2d agoLive · 3m ago

TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval

Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis. Structured and rule-based retrieval systems can explicitly target driving events, but typically require expert-defined rules, auxiliary data, and multi-stage perception pipelines. Multimodal embedding models offer a simpler and more efficient alternative by representing each video with a single searchable vector. However, general-purpose models often rely on shortcuts from static scene context and struggle to distinguish motion-centric events, such as turn

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  • FuzzyOverlapping authors or contributors · 62%mudler/LocalAI

    Shared author/contributor keys: guo

  • FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning

    Fuzzy title match (0.73): “TraVEL: Trajectory-Guided Video Embedding Learning for Drivi” ≈ “aymericdamien/TopDeepLearning”

  • FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses

    Fuzzy title match (0.73): “TraVEL: Trajectory-Guided Video Embedding Learning for Drivi” ≈ “Developer-Y/cs-video-courses”

  • LinkedLinked via arxiv author · 85%Yi-Chung Chen

    TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval

  • LinkedLinked via arxiv author · 85%Philip Jacobson

    TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval

  • LinkedLinked via arxiv author · 85%Tom Lampo

    TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval

  • LinkedLinked via arxiv author · 85%Yiren Lu

    TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval

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