A Perceptual Prediction Framework for Self Supervised Event Segmentation
This addresses the problem of reducing annotation needs for video segmentation, though it is incremental as it builds on existing self-supervised and cognitive-inspired methods.
The paper tackles self-supervised temporal segmentation of long videos without supervision, introducing a predictive learning framework that outperforms weakly-supervised and unsupervised approaches by up to 24% and shows competitive performance against fully supervised methods.
Temporal segmentation of long videos is an important problem, that has largely been tackled through supervised learning, often requiring large amounts of annotated training data. In this paper, we tackle the problem of self-supervised temporal segmentation of long videos that alleviate the need for any supervision. We introduce a self-supervised, predictive learning framework that draws inspiration from cognitive psychology to segment long, visually complex videos into individual, stable segments that share the same semantics. We also introduce a new adaptive learning paradigm that helps reduce the effect of catastrophic forgetting in recurrent neural networks. Extensive experiments on three publicly available datasets - Breakfast Actions, 50 Salads, and INRIA Instructional Videos datasets show the efficacy of the proposed approach. We show that the proposed approach is able to outperform weakly-supervised and other unsupervised learning approaches by up to 24% and have competitive performance compared to fully supervised approaches. We also show that the proposed approach is able to learn highly discriminative features that help improve action recognition when used in a representation learning paradigm.