  The problem of object localization has become one of the mainstream problems
of vision. Most of the algorithms proposed involve the design for the model to
be specifically for localizing objects. In this paper, we explore whether a
pre-trained canonical ConvNet (without fine-tuning) trained purely for object
classification on one dataset with global image level labels can be used to
localize objects in images containing a single instance on a separate dataset
while generalizing to novel classes. We propose a simple algorithm involving
cropping and blackening out regions in the image space called Explicit Image
Space based Search (EISS) for locating the most responsive regions in an image
in the context of object localization. EISS brings to light the interesting
phenomenon of a ConvNets responding more to features within objects as opposed
to object level descriptors, as the classes in the training data get more
correlated (visually/semantically similar).
