Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding

2024-04-30 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Expert, extended

Summary

LyraV introduces Streaming Video-Language Synchrony (SVLS), a novel paradigm for online video understanding that enables Video-LLMs to interleave perception and generation at frame-token granularity. This live streaming assistant, built upon a hierarchical control framework, features two key innovations: the Frame-Driven Transition Controller (FDTC) and the Streaming Token Pacer (SToP). FDTC is a training-free, verification-based finite-state machine that makes high-level semantic decisions on when to speak, start a new response, or remain silent. SToP is a lightweight predictive module that dynamically adjusts the language generation rate to match visual content. LyraV performs per-frame incremental, sub-budget decoding, emitting small token chunks within each frame interval to prevent perception blockage. Extensive experiments across five online and three offline benchmarks demonstrate LyraV preserves the backbone's general understanding while substantially improving streaming synchrony and narrative fluency, achieving 98.29% synchrony with video playback and a real-time processing speed of 3.89 FPS.

Key takeaway

For AI Engineers developing real-time video assistants, you should prioritize fine-grained video-language synchrony to eliminate stuttering and enhance user experience. Implement a hierarchical control framework like LyraV's FDTC and SToP to dynamically manage response timing and token generation rates. This approach ensures continuous perception while speaking, significantly improving narrative fluency and real-time processing speed in streaming applications.

Key insights

Streaming Video-Language Synchrony enables real-time, stutter-free video-language interaction by interleaving perception and generation at frame-token granularity.

Principles

• Interleave video frames with word tokens.
• Dynamically adapt language generation rate.
• Use perplexity for narrative coherence.

Method

LyraV uses FDTC, a perplexity-based finite-state machine, for high-level narrative control (Triggered, Continuing, Silent). SToP adaptively predicts and caps per-frame token emission based on visual density and real-time latency constraints.

In practice

• Implement per-frame incremental decoding for continuous output.
• Use a perplexity-based FSM for dynamic response timing.
• Train a lightweight pacer to adapt token emission rates.

Topics

• Streaming Video-Language Synchrony
• LyraV
• Frame-Driven Transition Controller
• Streaming Token Pacer
• Online Video-LLMs
• Real-time Video Understanding

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding — LyraV enables seamless video-language synchrony by interleaving per-frame video perception with sub-budget token generation, preventing pauses in streaming Video-LLMs.
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• ST-SimDiff: Balancing Spatiotemporal Similarity and Difference for Efficient Video Understanding with MLLMs — Efficient video understanding balances compressing redundant content with preserving dynamic, event-driven changes.
• PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning — PyraVid uses hierarchical multimodal memory for long-horizon video reasoning, inspired by cognitive science.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.