  Recently there has been growing interest in building active visual object
recognizers, as opposed to the usual passive recognizers which classifies a
given static image into a predefined set of object categories. In this paper we
propose to generalize these recently proposed end-to-end active visual
recognizers into a controller-recognizer framework. A model in the
controller-recognizer framework consists of a controller, which interfaces with
an external manipulator, and a recognizer which classifies the visual input
adjusted by the manipulator. We describe two most recently proposed
controller-recognizer models: recurrent attention model and spatial transformer
network as representative examples of controller-recognizer models. Based on
this description we observe that most existing end-to-end
controller-recognizers tightly, or completely, couple a controller and
recognizer. We ask a question whether this tight coupling is necessary, and try
to answer this empirically by building a controller-recognizer model with a
decoupled controller and recognizer. Our experiments revealed that it is not
always necessary to tightly couple them and that by decoupling a controller and
recognizer, there is a possibility of building a generic controller that is
pretrained and works together with any subsequent recognizer.
