A Simple Baseline for Streaming Video Understanding
Yujiao Shen, Shulin Tian, Jingkang Yang, Ziwei Liu
2026-04-06
Summary
This paper investigates how well simple methods can perform when trying to understand videos as they're being streamed, rather than having the whole video available at once.
What's the problem?
Many recent approaches to understanding streaming video rely on complicated systems to 'remember' information from earlier parts of the video, assuming this is crucial for good performance. The problem is that these complex systems are hard to build and it's not clear if they're actually *necessary* for understanding what's happening in the video right now.
What's the solution?
The researchers showed that a surprisingly simple method – just feeding the last few frames of a video into a standard video understanding model – works just as well, or even better, than many of these more complex streaming video models. They called this method 'SimpleStream' and tested it on several standard benchmarks, consistently finding it performed strongly, even with only 4 recent frames.
Why it matters?
This work suggests that focusing on improving a model's ability to understand the *current* scene is more important than building bigger and bigger 'memories' for video understanding. It also argues that future tests of streaming video models should specifically measure how well they understand the present moment versus how well they recall the past, so we can better evaluate if new, complex methods are truly making progress.
Abstract
Recent streaming video understanding methods increasingly rely on complex memory mechanisms to handle long video streams. We challenge this trend with a simple finding: a sliding-window baseline that feeds only the most recent N frames to an off-the-shelf VLM already matches or surpasses published streaming models. We formalize this baseline as SimpleStream and evaluate it against 13 major offline and online video LLM baselines on OVO-Bench and StreamingBench. Despite its simplicity, SimpleStream delivers consistently strong performance. With only 4 recent frames, it reaches 67.7% average accuracy on OVO-Bench and 80.59% on StreamingBench. Controlled ablations further show that the value of longer context is backbone-dependent rather than uniformly increasing with model scale, and reveal a consistent perception-memory trade-off: adding more historical context can improve recall, but often weakens real-time perception. This suggests that stronger memory, retrieval, or compression modules should not be taken as evidence of progress unless they clearly outperform SimpleStream under the same protocol. We therefore argue that future streaming benchmarks should separate recent-scene perception from long-range memory, so that performance improvements from added complexity can be evaluated more clearly.