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Temporal segmentation of long videos is an important problem, that haslargely been tackled through supervised learning, often requiring large amountsof annotated training data. In this paper, we tackle the problem ofself-supervised temporal segmentation of long videos that alleviate the needfor any supervision. We introduce a self-supervised, predictive learningframework that draws inspiration from cognitive psychology to segment long,visually complex videos into individual, stable segments that share the samesemantics. We also introduce a new adaptive learning paradigm that helps reducethe effect of catastrophic forgetting in recurrent neural networks. Extensiveexperiments on three publicly available datasets - Breakfast Actions, 50Salads, and INRIA Instructional Videos datasets show the efficacy of theproposed approach. We show that the proposed approach is able to outperformweakly-supervised and other unsupervised learning approaches by up to 24% andhave competitive performance compared to fully supervised approaches. We alsoshow that the proposed approach is able to learn highly discriminative featuresthat help improve action recognition when used in a representation learningparadigm.

A Perceptual Prediction Framework for Self Supervised Event Segmentation