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FlowMimic presents a method for mask-free visual editing and generation across video and image modalities using pixel-pair warped flow fields, enabling real-time video editing data generation from image editing samples and aligning modality capabilities through mimicry losses.
Proposes CF-JEPA, a mask-free self-supervised learning framework for time-series that uses multi-horizon forward prediction from random crops and exploits asymmetry between online and target encoders for improved performance on classification, forecasting, and anomaly detection.