V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
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 training signal, encouraging explicit spatial and temporal grounding. Second, deep self-supervision applies the self-supervised objective hierarchically across multiple intermediate encoder layers to improve representation quality. Third, multi-modal tokenizers enable unified training across images and videos. Finally, the model benefits from...
| Authors | Lorenzo Mur-Labadia, Matthew Muckley, Amir Bar et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 March 2026 |
| Last updated at source | 11 June 2026 |
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