UNSUPERVISED LEARNING FOR FORECASTING ACTION REPRESENTATIONS

Most of previous works on future forecasting require a mass of videos with frame-level labels which would probably limit their application, since labelling video frame requires much tremendous efforts. In this paper, we present a unsupervised learning framework to anticipate the future representation by utilizing temporal historical information and train the anticipating capacity only using unlabelled videos. Compared to existing methods that predict the future representation from a static image, our proposed model presents a novel temporal context learning model for estimating the temporal evolution tendency by compacting outputs of all time steps in a LST-M. We evaluate the proposed model on two different activity datasets, TV Human Interaction dataset and THUMOS Validation and Test sets. We have demonstrated the effectiveness of our model in anticipating future representation task.