  This paper aims to classify and locate objects accurately and efficiently,
without using bounding box annotations. It is challenging as objects in the
wild could appear at arbitrary locations and in different scales. In this
paper, we propose a novel classification architecture ProNet based on
convolutional neural networks. It uses computationally efficient neural
networks to propose image regions that are likely to contain objects, and
applies more powerful but slower networks on the proposed regions. The basic
building block is a multi-scale fully-convolutional network which assigns
object confidence scores to boxes at different locations and scales. We show
that such networks can be trained effectively using image-level annotations,
and can be connected into cascades or trees for efficient object
classification. ProNet outperforms previous state-of-the-art significantly on
PASCAL VOC 2012 and MS COCO datasets for object classification and point-based
localization.

Computer Vision and Pattern Recognition