AI RESEARCH
V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
arXiv CS.CV
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ArXi:2603.14482v1 Announce Type: new We present V-JEPA 2.1, a family of self-supervised models that learn dense, high-quality visual representations for both images and videos while retaining strong global scene understanding. The approach combines four key components. First, a dense predictive loss uses a masking-based objective in which both visible and masked tokens contribute to the