ProtoKV: Streaming Video Understanding under Delayed Query with Summary-State Memory
This work provides a constant-footprint memory solution for streaming video understanding, which is critical for applications with strict latency and memory budgets.
ProtoKV addresses streaming video understanding under delayed queries by using a summary-state memory that aggregates far history into prototypes, improving accuracy by up to 12.5 points over token-retention baselines in long-delay regimes.
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 keeps an exact near-window KV cache and aggregates older content into a semantic-spatial prototype bank with residual statistics. At query time, each prototype is exposed through a bounded pseudo-token interface that is drop-in compatible with standard attention. Under matched budgets and comparable query-time cost, ProtoKV improves accuracy by up to 12.5 points over token-retention baselines on SVU benchmarks in the long-delay regime, with gains that grow as query delay increases.