  Localizing objects with weak supervision in an image is a key problem of the
research in computer vision community. Many existing Weakly-Supervised Object
Localization (WSOL) approaches tackle this problem by estimating the most
discriminative regions with feature maps (activation maps) obtained by Deep
Convolutional Neural Network, that is, only the objects or parts of them with
the most discriminative response will be located. However, the activation maps
often display different local maximum responses or relatively weak response
when one image contains multiple objects with the same type or small objects.
In this paper, we propose a simple yet effective multi-scale discriminative
region discovery method to localize not only more integral objects but also as
many as possible with only image-level class labels. The gradient weights
flowing into different convolutional layers of CNN are taken as the input of
our method, which is different from previous methods only considering that of
the final convolutional layer. To mine more discriminative regions for the task
of object localization, the multiple local maximum from the gradient weight
maps are leveraged to generate the localization map with a parallel sliding
window. Furthermore, multi-scale localization maps from different convolutional
layers are fused to produce the final result. We evaluate the proposed method
with the foundation of VGGnet on the ILSVRC 2016, CUB-200-2011 and PASCAL VOC
2012 datasets. On ILSVRC 2016, the proposed method yields the Top-1
localization error of 48.65\%, which outperforms previous results by 2.75\%. On
PASCAL VOC 2012, our approach achieve the highest localization accuracy of
0.43. Even for CUB-200-2011 dataset, our method still achieves competitive
results.
