Seeing Fast and Slow: Learning the Flow of Time in Videos Time is everywhere in video — yet most computer vision models treat it as an afterthought. We compress temporal information into feature vectors, shuffle frames during training, and generally act like order doesn't matter. A new paper from researchers at the University of Washington and Google challenges that assumption head-on, treating time itself as a learnable visual concept. Key Idea The core insight is deceptively simple: if you can tell whether a video has been sped up or slowed down, you fundamentally understand something about how motion unfolds in the real world. The paper frames temporal perception as a self-supervised learning problem — no manual labels needed. Rather than annotating playback speed by hand, the authors exploit a signal that's already baked into videos: natural multimodal cues . Audio pitch, optical flow magnitude, and the statistical texture of motion all shift predictably when you change playback speed.…