Humans and machines see the world differently, each having their own strengths
and weaknesses. In this talk, I describe two projects exploring how they may
help each other.

Visual object recognition by machines is notoriously difficult. To help in the
learning process, humans are typically used to gather large hand-labeled
training datasets from which the machines may learn. However, humans may also
be used to “debug” the machine’s recognition pipeline to learn what
aspects are lacking. Specifically, we explore the various stages of part-based
person detectors. We perform human studies in which subjects perform the same
sub-tasks as their machine counterparts, and accuracies are compared.

The typical human has significant difficultly in drawing everyday objects
containing complex structures, such as faces or bikes. When learning to draw,
humans must learn to see the word differently. That is, they must not only
recognize what they are seeing, but they must perceive the spacing and
structural layout of an object. We demonstrate an application in which machines
can recognize what a human is drawing and provide visual guidance to the drawer
in the form of shadows. The shadows, which may be either used or ignored by the
drawer, help the drawer achieve more realistic overall shapes and spacing, while
maintaining their own unique drawing style.