V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.
Nature neuroscience , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
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Online generalised predictive coding (ODEM) tracks latent states in nonlinear and chaotic generative models by separating temporal scales for fast Bayesian belief updating and slow parameter learning.
citing papers explorer
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Revisiting Feature Prediction for Learning Visual Representations from Video
V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.
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Online Generalised Predictive Coding
Online generalised predictive coding (ODEM) tracks latent states in nonlinear and chaotic generative models by separating temporal scales for fast Bayesian belief updating and slow parameter learning.
- Learning to Theorize the World from Observation