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arXiv · 2504.13915

Memory-efficient Streaming VideoLLMs for Real-time Procedural Video Understanding

Dibyadip Chatterjee·Edoardo Remelli·Yale Song·Bugra Tekin·Abhay Mittal·Bharat Bhatnagar·Necati Cihan Camgöz·Shreyas Hampali·Eric Sauser·Shugao Ma·Angela Yao·Fadime Sener
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Abstract

We introduce ProVideLLM, an end-to-end framework for real-time procedural video understanding. ProVideLLM integrates a multimodal cache configured to store two types of tokens - verbalized text tokens, which provide compressed textual summaries of long-term observations, and visual tokens, encoded with DETR-QFormer to capture fine-grained details from short-term observations. This design reduces token count by 22x over existing methods in representing one hour of long-term observations while effectively encoding fine-granularity of the present. By interleaving these tokens in our multimodal cache, ProVideLLM ensures sub-linear scaling of memory and compute with video length, enabling per-frame streaming inference at 10 FPS and streaming dialogue at 25 FPS, with a minimal 2GB GPU memory footprint. ProVideLLM also sets new state-of-the-art results on six procedural tasks across four datasets.

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Dibyadip Chatterjee, Edoardo Remelli, Yale Song, Bugra Tekin, Abhay Mittal, Bharat Bhatnagar, Necati Cihan Camgöz, Shreyas Hampali, Eric Sauser, Shugao Ma, Angela Yao, Fadime Sener. 2025-04-10. Memory-efficient Streaming VideoLLMs for Real-time Procedural Video Understanding. https://arxiv.org/abs/2504.13915

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