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  "text": "A new paper from @ylecun and others – V-JEPA 2.1\n\nIt changes the recipe of V-JEPA so the model learns both:\n\n• Global semantics – what is happening in the scene\n• Dense spatio-temporal structure – where things are and how they move\n\nThe idea is to supervise not just masked tokens but the visible ones too\n\nThere are 4 key ingredients for V-JEPA 2.1:\n\n- Dense prediction loss on both masked and visible tokens\n- Deep self-supervision across intermediate layers\n- Modality-specific tokenizers (2D for images, 3D for videos) within a shared encoder\n- Model + data scaling\n\nThe workflow turns into: masked image/video → encode visible tokens → predict latent representations for both masked and visible tokens → supervise at multiple layers\n\nHere are the details:",
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