Developmental Visual Agents (DVA) are intelligent agents aimed at learning to see like children. They are thought of as general-purpose systems capable of continuously processing video information, according to the never-ending learning philosophy, and they are designed for interacting with users, which provide them supervisions on the objects in the scene. DVA are organized in a hierarchical architecture, which first extracts scale- and rotation-invariant features at the bottom levels, and then processes such features in order to identify regions and recognize objects according to the framework of Support Constraint Machines.
Eye Movement Laws (EYMOL) is a differential model of attentional scanpath. We devise variational laws of the eye-movement that rely on a generalized view of the Least Action Principle in physics. The potential energy captures details as well as peripheral visual features, while the kinetic energy corresponds with the classic interpretation in analytic mechanics. In addition, the Lagrangian contains a brightness invariance term, which characterizes significantly the scanpath trajectories. We obtain differential equations of visual attention as the stationary point of the generalized action. Model is evaluated in tasks of saliency prediction and scanpath prediction.