arXiv:2606.26762v1 Announce Type: cross Abstract: Streaming video understanding (SVU) must answer queries that arrive asynchronously while visual tokens stream continuously under strict GPU-memory and query-time latency budgets. A key challenge is delayed query: decisive cues may appear briefly, yet many subsequent updates occur before the query arrives, increasing the risk that those cues are evicted or diluted under bounded memory. We propose ProtoKV, a constant-footprint SVU memory that represents far history as a fixed-capacity summary state rather than retaining token instances. ProtoKV k
The continuous growth in demand for real-time video analysis and the increasing complexity of AI models necessitate more efficient memory management solutions.
This development addresses critical limitations in streaming video understanding, enabling more robust and scalable AI applications that are crucial for various industries.
AI systems will be able to process streaming video more effectively under strict resource constraints, improving responsiveness and analytical depth without compromising historical context.
- · AI Vision Systems Developers
- · Security & Surveillance Industry
- · Autonomous Vehicle Manufacturers
- · Cloud Computing Providers
- · Legacy Video Processing Architectures
- · Hardware-constrained Edge AI Devices
More sophisticated and real-time AI agents can operate on continuous video feeds with reduced computational overhead.
This improved capability leads to advancements in human-machine interaction and autonomous decision-making in dynamic environments.
The widespread adoption of such efficient video understanding could accelerate the deployment of intelligent infrastructure and smart city initiatives.
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Read at arXiv cs.LG