Streaming Video Question-Answering with In-context Video KV-Cache Retrieval
Keywords: Video Understanding, Multimodal Large Language Models, Streaming Video Question-answering
TL;DR: We propose a training-free approach that integrates with existing Video-LLMs for streaming video question-answering.
Abstract: We propose ReKV, a novel, training-free approach that integrates seamlessly with existing Video Large Language Models (Video-LLMs) to enable efficient streaming video question-answering (StreamingVQA). Traditional VideoQA systems struggle with long videos, as they must process the entire video before responding to queries, and repeat this process for each new question. In contrast, our approach analyzes long videos in a streaming fashion, allowing for prompt responses as soon as user queries are received. Building on a common Video-LLM, we first incorporate a sliding-window attention mechanism, ensuring that input frames attend to a limited number of preceding frames, thereby reducing computational overhead. To prevent information loss, we store processed video key-value caches (KV-Caches) in RAM and disk, reloading them into GPU memory as needed. Additionally, we introduce a retrieval method that leverages an external retriever or the parameters within Video-LLMs to retrieve only query-relevant KV-Caches, ensuring both efficiency and accuracy in question answering. ReKV enables the separation of video analyzing and question-answering across different processes and GPUs, significantly enhancing the efficiency of StreamingVQA. Through comprehensive experimentation, we validate the efficacy and practicality of our approach, which significantly boosts efficiency and enhances applicability over existing VideoQA models.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 861
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