  Weakly supervised learning of object detection is an important problem in
image understanding that still does not have a satisfactory solution. In this
paper, we address this problem by exploiting the power of deep convolutional
neural networks pre-trained on large-scale image-level classification tasks. We
propose a weakly supervised deep detection architecture that modifies one such
network to operate at the level of image regions, performing simultaneously
region selection and classification. Trained as an image classifier, the
architecture implicitly learns object detectors that are better than
alternative weakly supervised detection systems on the PASCAL VOC data. The
model, which is a simple and elegant end-to-end architecture, outperforms
standard data augmentation and fine-tuning techniques for the task of
image-level classification as well.
