Original title: View while moving: a unified multi-modal paradigm for efficient recognition in long-untrimmed videos
Mengyu Yang, Ye Tian, Lanshan Zhang, Jingyi Wang +4 · September 2026
Long, unedited videos contain lots of repetitive details and noise. Most computer systems handle them in two separate stages: they preview the video first, and then run full recognition later. This slows things down. Researchers introduced a new system called ViMo, which stands for View while Moving. Instead of doing two steps, ViMo combines sampling and recognition into one single pass. It views the raw video frames just one time during inference. It uses a hierarchy to first grasp small unit-level details and then figure out bigger video meanings. They also used game theory to build ViMo+, which handles multiple video data types together. Testing on both long-untrimmed and short-trimmed videos shows these methods reach state-of-the-art accuracy while greatly cutting inference costs.
Imagine you're reading a long book, but you have to decide which sentences to read before you start reading them. That's inefficient because you might skip something important. Now suppose you read and decide at the same time, so you never miss key details. In this paper, the system watches a video and decides which parts matter in one continuous process, rather than previewing first and then analyzing separately.
Long videos from surveillance, sports, or social media are expensive to analyze because most frames are redundant. This method cuts that cost by merging sampling and recognition into one pass, and extends to multiple data types like video and audio. It makes real-time video understanding faster and more practical for real-world applications.
The abstract does not discuss specific limitations. It reports state-of-the-art accuracy on both long-untrimmed and short-trimmed videos, but does not mention failure cases, computational overhead of the game theory component, or scalability to extremely long videos beyond the tested datasets.
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View while moving: a unified multi-modal paradigm for efficient recognition in long-untrimmed videos — Mengyu Yang, Ye Tian, Lanshan Zhang et al. — Tsinghua Science & Technology — doi:10.26599/tst.2026.9010083
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.