  The deep generative adversarial networks (GAN) recently have been shown to be
promising for different computer vision applications, like image edit- ing,
synthesizing high resolution images, generating videos, etc. These networks and
the corresponding learning scheme can handle various visual space map- pings.
We approach GANs with a novel training method and learning objective, to
discover multiple object instances for three cases: 1) synthesizing a picture
of a specific object within a cluttered scene; 2) localizing different
categories in images for weakly supervised object detection; and 3) improving
object discov- ery in object detection pipelines. A crucial advantage of our
method is that it learns a new deep similarity metric, to distinguish multiple
objects in one im- age. We demonstrate that the network can act as an
encoder-decoder generating parts of an image which contain an object, or as a
modified deep CNN to rep- resent images for object detection in supervised and
weakly supervised scheme. Our ranking GAN offers a novel way to search through
images for object specific patterns. We have conducted experiments for
different scenarios and demonstrate the method performance for object
synthesizing and weakly supervised object detection and classification using
the MS-COCO and PASCAL VOC datasets.
