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MotionJEPA: Preventing Temporal Feature Collapse by Capturing Visual Changes in Latent Space
Joint Embedding Predictive Architectures (JEPAs) are a promising paradigm for learning task-agnostic latent world models without visual reconstruction. However, standard JEPA training exhibits a strong inductive bias towards slow features, causing feature suppression and the collapse of latent repre
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