  We describe a computational model of humans' ability to provide a detailed
interpretation of components in a scene. Humans can identify in an image
meaningful components almost everywhere, and identifying these components is an
essential part of the visual process, and of understanding the surrounding
scene and its potential meaning to the viewer. Detailed interpretation is
beyond the scope of current models of visual recognition. Our model suggests
that this is a fundamental limitation, related to the fact that existing models
rely on feed-forward but limited top-down processing. In our model, a first
recognition stage leads to the initial activation of class candidates, which is
incomplete and with limited accuracy. This stage then triggers the application
of class-specific interpretation and validation processes, which recover richer
and more accurate interpretation of the visible scene. We discuss implications
of the model for visual interpretation by humans and by computer vision models.
