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    3. RoarGraph — A Projected Bipartite Graph for Efficient Cross-Modal ANNS

    RoarGraph — A Projected Bipartite Graph for Efficient Cross-Modal ANNS

    VLDB 2024 paper proposing RoarGraph, a projected bipartite graph structure for efficient cross-modal approximate nearest neighbor search. The method addresses the challenges of searching across different modalities (e.g., text, image) using graph-based indexing.

    Overview

    RoarGraph introduces a projected bipartite graph structure for efficient cross-modal approximate nearest neighbor search, enabling effective retrieval across different data modalities.

    Key Contributions

    • Novel bipartite graph construction for cross-modal search
    • Projection-based graph navigation for efficient traversal
    • Handles text-image and other cross-modal retrieval tasks
    • Demonstrates strong performance on cross-modal benchmarks

    Publication

    • Venue: VLDB 2024
    • Authors: Chen et al.
    • Abbreviation: RoarGraph

    Impact

    RoarGraph provides a graph-based solution for cross-modal retrieval, which is increasingly important for multi-modal AI applications including retrieval-augmented generation scenarios involving different data types.

    Surveys

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    Information

    Websitewww.vldb.org
    PublishedApr 4, 2026

    Categories

    1 Item
    Research Papers & Surveys

    Tags

    3 Items
    #cross-modal#graph-index#ann

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