SOURCE-LINKED INTELLIGENCE
LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics
Video carries the temporal structure of the physical world, yet learning representations from it has remained computationally expensive: prevailing self-supervised methods either prevent representation collapse through architectural asymmetries, coupling an exponential-moving-average target encoder, a stop-gradient, and a capacity-limited predictor, or circumvent it by reconstructing masked content in pixel space. We introduce LeVJEPA, the first video encoder trained under LeJEPA's collapse-free objective, which dispenses with both. A single encoder is trained with an invariance loss over glob
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T17:26:24.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.