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Related Experiment Videos

Knowledge Diffusion-Based Adaptive Alignment with Hierarchical Context for Video Temporal Grounding.

Ran Ran, Jiwei Wei, Yang Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 19, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Knowledge Diffusion Alignment (KDA), a novel framework for Video Temporal Grounding (VTG). KDA effectively bridges the semantic gap between text queries and video content, improving moment localization accuracy.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Video Temporal Grounding (VTG) aims to precisely locate specific moments in videos based on natural language queries.
    • Key challenges in VTG include bridging the semantic gap between text and visuals and extracting relevant information from lengthy, background-heavy videos.

    Purpose of the Study:

    • To propose Knowledge Diffusion Alignment (KDA), a diffusion-based framework designed to enhance Video Temporal Grounding.
    • To address the semantic gap and improve the extraction of discriminative moment cues in untrimmed videos.

    Main Methods:

    • Developed a Background Residual Diffusion Model (BRDM) to synthesize moment knowledge by removing irrelevant background information.
    • Introduced Hierarchical Video Knowledge Extraction (HVKE) for multi-scale context aggregation and conditional priors.
    • Employed a Dynamic Moment-aware Encoder (DME) and Spans-Enhanced Hierarchical Decoder (SEHD) for adaptive temporal modeling and prediction.

    Main Results:

    • The proposed KDA framework demonstrated superior performance compared to existing state-of-the-art methods on public benchmarks.
    • Extensive ablation studies confirmed the efficacy of the individual module designs within KDA.

    Conclusions:

    • Knowledge Diffusion Alignment (KDA) offers a robust solution for Video Temporal Grounding by effectively synthesizing and aligning semantic knowledge.
    • The framework's modular design and diffusion-based approach significantly advance the capabilities of moment localization in videos.