AI看视频终于学会“回看”了
现在的AI看监控或长视频，是一边看一边压缩记忆，但压缩时会把关键细节丢掉，而且一旦丢了就再也找不回来——就像你边看边记笔记，漏掉的那几秒永远想不起来。DeltaStreamer换了个思路：它先不急着压缩，而是把画面里“发生了什么变化”单独记下来，比如有人走过、灯亮了、门开了，这些事件轨迹单独存着；等有人提问时，它再根据问题去翻历史画面，把当时漏掉的视觉细节重新找回来，再结合记忆作答。在OVO-Bench和StreamingBench两个评测上，它比现有方法分别高出不少，总分66.07%和59.53%。它不是你明天就能用的产品，但“先记变化、再按需回看”这个思路，可能是让AI真正看懂长视频的关键一步。
📄 原文摘要(英文)
We propose DeltaStreamer, a novel agentic framework for streaming video understanding inspired by how humans observe, remember, and investigate. Streaming video understanding requires a system to retain evidence before knowing which details future queries will need. Existing approaches often obscure event transitions and omit relevant details when compressing video history, and cannot recover missing visual evidence. DeltaStreamer addresses these limitations by combining change-aware memory with query-driven planning and targeted evidence gathering with replay. Before a query arrives, it continuously observes the stream and explicitly records visual changes in structured memory, preserving how events unfold rather than only what is present. When a query arrives, a query-driven planner identifies the required evidence and guides memory retrieval and targeted replay. By revisiting historical frames, the agent can recover visual details omitted during observation and ground its answer in evidence beyond the retained descriptions. This connects continuous observation with active investigation tailored to each question. Experiments on OVO-Bench and StreamingBench demonstrate improvements over representative streaming video understanding methods, with overall scores of 66.07% and 59.53%, respectively. Project page: https://bbbbchan.github.io/DeltaStreamer/