We, humans, are always making predictions about our environment. These predictions are made based on various factors, and as we all know, some things are easier to foretell than others. Researchers at Columbia University have proposed a new framework for and hierarchical predictive model that can learn what is predictable from the unlabelled videos.
In their paper, Learning the Predictability of the Future, they have introduced a hierarchical model. This model is based on the inspiration that often, people organize their actions hierarchically. The researchers have developed an approach to jointly learn a hierarchy of activities from an unlabelled video and learn to anticipate them at the right level of abstraction. The model can predict future action when it is confident. When it lacks confidence, it will select a higher level of abstraction to improve confidence, i.e., when the future is certain, the model will predict the future as precisely as possible. In case the future is uncertain, the model should ‘hedge the bet’ and foretell a hierarchical parent.