  We propose a novel object localization methodology with the purpose of
boosting the localization accuracy of state-of-the-art object detection
systems. Our model, given a search region, aims at returning the bounding box
of an object of interest inside this region. To accomplish its goal, it relies
on assigning conditional probabilities to each row and column of this region,
where these probabilities provide useful information regarding the location of
the boundaries of the object inside the search region and allow the accurate
inference of the object bounding box under a simple probabilistic framework.
  For implementing our localization model, we make use of a convolutional
neural network architecture that is properly adapted for this task, called
LocNet. We show experimentally that LocNet achieves a very significant
improvement on the mAP for high IoU thresholds on PASCAL VOC2007 test set and
that it can be very easily coupled with recent state-of-the-art object
detection systems, helping them to boost their performance. Finally, we
demonstrate that our detection approach can achieve high detection accuracy
even when it is given as input a set of sliding windows, thus proving that it
is independent of box proposal methods.

Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing