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paperarXivTrust 82 · PrimaryPublished 8d agoLive · 7d ago

ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding

Streaming video understanding is a critical capability for real-world applications, including embodied intelligence, autonomous driving, industrial monitoring, surveillance and early warning, and wearable assistants. However, processing continuous video streams with multimodal large language models (MLLMs) is computationally expensive. Existing efforts have explored reducing streaming overhead through visual token pruning, token merging, quantization, on-demand frame retrieval, and context offloading. However, most existing methods overlook the dimension of model depth. Repeatedly executing fu

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  • FuzzyOverlapping authors or contributors · 62%shareAI-lab/learn-claude-code

    Shared author/contributor keys: yue

  • FuzzySimilar title/name (fuzzy) · 59%VectifyAI/PageIndex

    Fuzzy title match (0.73): “ShallowStream: Index Shallow then Answer Deep for Streaming ” ≈ “VectifyAI/PageIndex”

  • FuzzySimilar title/name (fuzzy) · 59%run-llama/llama_index

    Fuzzy title match (0.73): “ShallowStream: Index Shallow then Answer Deep for Streaming ” ≈ “run-llama/llama_index”

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

    Fuzzy title match (0.73): “ShallowStream: Index Shallow then Answer Deep for Streaming ” ≈ “Developer-Y/cs-video-courses”

  • LinkedLinked via arxiv author · 85%Jitai Hao

    ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding

  • LinkedLinked via arxiv author · 85%Ke Yang

    ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding

  • LinkedLinked via arxiv author · 85%Qiang Huang

    ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding

  • LinkedLinked via arxiv author · 85%Zhengjun Yue

    ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding

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