Continual Object Learning

This work focuses on building frameworks to strengthen the relation between human and machine learning. This is achieved by proposing a new category of algorithms and a new theory to formalize the perception and categorizationof objects. For what concerns the algorithmic part, we developed a series of procedures to perform Interactive Continuous Open World learning from the point of view of a single user. As for humans, the input of the algorithms are continuous streams of visual information (sequences of frames), that enable the extraction of richer representations by exploiting the persistence of the same object in the input data. Our approaches are able to incrementally learn and recognize collections of objects, starting from emph{zero} knowledge, and organizing them in a hierarchy that follows the will of the user. We then present a novel Knowledge Representation theory that formalizes the property of our setting and enables the learning over it. The theory is based on the notion of separating the visual representation of objects from the semantic meaning associated with them. This distinction enables to treat both instances and classes of objects as being elements of the same kind, as well as allowing for dynamically rearranging objects according to the needs of the user. The whole framework is gradually introduced through the entire thesis and is coupled with an extensive series of experiments to demonstrate its working principles. The experiments focus also on demonstrating the role of a developmental learning policy, in which new objects are regularly introduced, enabling both an increase in recognition performance while reducing the amount of supervision provided by the user.