JEPA Predictors Enable Occluded Feature Completion Across Encoder Families
A new study demonstrates that predictors from Joint-Embedding Predictive Architectures (JEPAs) can be transferred to non-JEPA encoders such as CLIP and DINOv2 using a single linear projection. This approach significantly improves classification accuracy under heavy occlusion, with the frozen JEPA predictor boosting Stanford Dogs accuracy from 15.9% to 52.1% when paired with CLIP. The benefit increases with the degree of occlusion, though the linear projection is less effective at low occlusion levels.
Why it matters: This work shows that JEPA predictors can serve as portable operators for occluded feature completion, potentially enabling more robust classification from partial views without retraining.
Full story at: arXiv Computer Vision ↗