What is the inference latency overhead of VideoRAG's retrieval-augmented approach compared to vanilla video un

Abstract

Abstract: Large Language Models (LLMs) suffer from hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented Generation (RAG) mitigates these issues by integrating external dynamic information for improved factual grounding. With advances in multimodal learning, Multimodal RAG extends this approach by incorporating multiple modalities such as text, images, audio, and video to enhance the generated outputs. However, cross-modal alignment and reasoning introduce unique challenges beyond those in unimodal RAG. This survey offers a structured and comprehensive

Research Question

What is the inference latency overhead of VideoRAG's retrieval-augmented approach compared to vanilla video understanding models when processing extreme-length video contexts (>100K frames) on Adept-1B and similar multimodal benchmarks?

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