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Series: AI Papers
Revisiting Feature Prediction for Learning Visual Representations from Video (V-JEPA) — Adrien Bardes and colleagues
V-JEPA learns visual representations by predicting features of missing regions in video.
The approach trains a model in representation space rather than reconstructing every pixel.
The paper investigates feature prediction as a standalone objective for learning without labels.
Learned representations are evaluated on tasks involving motion, appearance, and visual recognition.
The research explores how observing video can support more general visual understanding.
Explore: AI & Computer Science · AI Papers
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Parole chiave
learning
developmental
AI
computation
AI & Computer Science
Self-supervised learning
World models
Yann LeCun
AI Papers