  Weakly Supervised Object Localization (WSOL) methodsusually rely on fully
convolutional networks in order to ob-tain class activation maps(CAMs) of
targeted labels. How-ever, these networks always highlight the most
discriminativeparts to perform the task, the located areas are much smallerthan
entire targeted objects. In this work, we propose a novelend-to-end model to
enlarge CAMs generated from classifi-cation models, which can localize targeted
objects more pre-cisely. In detail, we add an additional module in
traditionalclassification networks to extract foreground object propos-als from
images without classifying them into specific cate-gories. Then we set these
normalized regions as unrestrictedpixel-level mask supervision for the
following classificationtask. We collect a set of images defined as Background
ImageSet from the Internet. The number of them is much smallerthan the targeted
dataset but surprisingly well supports themethod to extract foreground regions
from different pictures.The region extracted is independent from classification
task,where the extracted region in each image covers almost en-tire object
rather than just a significant part. Therefore, theseregions can serve as masks
to supervise the response mapgenerated from classification models to become
larger andmore precise. The method achieves state-of-the-art results
onCUB-200-2011 in terms of Top-1 and Top-5 localization er-ror while has a
competitive result on ILSVRC2016 comparedwith other approaches.
