  Object localization is an important task in computer vision but requires a
large amount of computational power due mainly to an exhaustive multiscale
search on the input image. In this paper, we describe a near real-time
multiscale search on a deep CNN feature map that does not use region proposals.
The proposed approach effectively exploits local semantic information preserved
in the feature map of the outermost convolutional layer. A multi-scale search
is performed on the feature map by processing all the sub-regions of different
sizes using separate expert units of fully connected layers. Each expert unit
receives as input local semantic features only from the corresponding
sub-regions of a specific geometric shape. Therefore, it contains more nearly
optimal parameters tailored to the corresponding shape. This multi-scale and
multi-aspect ratio scanning strategy can effectively localize a potential
object of an arbitrary size. The proposed approach is fast and able to localize
objects of interest with a frame rate of 4 fps while providing improved
detection performance over the state-of-the art on the PASCAL VOC 12 and MSCOCO
data sets.
