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Learning to model how the world changes as time elapses has proven achallenging problem for the computer vision community. We propose aself-supervised solution to this problem using temporal cycle consistencyjointly in vision and language, training on narrated video. Our model learnsmodality-agnostic functions to predict forward and backward in time, which mustundo each other when composed. This constraint leads to the discovery ofhigh-level transitions between moments in time, since such transitions areeasily inverted and shared across modalities. We justify the design of ourmodel with an ablation study on different configurations of the cycleconsistency problem. We then show qualitatively and quantitatively that ourapproach yields a meaningful, high-level model of the future and past. We applythe learned dynamics model without further training to various tasks, such aspredicting future action and temporally ordering sets of images. Project page:https://dave.ml/mmcc

Learning Temporal Dynamics from Cycles in Narrated Video