  The dominant paradigm for feature learning in computer vision relies on
training neural networks for the task of object recognition using millions of
hand labelled images. Is it possible to learn useful features for a diverse set
of visual tasks using any other form of supervision? In biology, living
organisms developed the ability of visual perception for the purpose of moving
and acting in the world. Drawing inspiration from this observation, in this
work we investigate if the awareness of egomotion can be used as a supervisory
signal for feature learning. As opposed to the knowledge of class labels,
information about egomotion is freely available to mobile agents. We show that
given the same number of training images, features learnt using egomotion as
supervision compare favourably to features learnt using class-label as
supervision on visual tasks of scene recognition, object recognition, visual
odometry and keypoint matching.
