  This paper proposes a simple, yet very effective method to localize dominant
foreground objects in an image, to pixel-level precision. The proposed method
'MASON' (Model-AgnoStic ObjectNess) uses a deep convolutional network to
generate category-independent and model-agnostic heat maps for any image. The
network is not explicitly trained for the task, and hence, can be used
off-the-shelf in tandem with any other network or task. We show that this
framework scales to a wide variety of images, and illustrate the effectiveness
of MASON in three varied application contexts.
