  Weakly supervised object localization (WSOL) is a challenging problem which
aims to localize objects with only image-level labels. Due to the lack of
ground truth bounding boxes, class labels are mainly employed to train the
model. This model generates a class activation map (CAM) which activates the
most discriminate features. However, the main drawback of CAM is the ability to
detect just a part of the object. To solve this problem, some researchers have
removed parts from the detected object \cite{b1, b2, b4}, or the image
\cite{b3}. The aim of removing parts from image or detected parts of the object
is to force the model to detect the other features. However, these methods
require one or many hyper-parameters to erase the appropriate pixels on the
image, which could involve a loss of information. In contrast, this paper
proposes a Hierarchical Complementary Learning Network method (HCLNet) that
helps the CNN to perform better classification and localization of objects on
the images. HCLNet uses a complementary map to force the network to detect the
other parts of the object. Unlike previous works, this method does not need any
extras hyper-parameters to generate different CAMs, as well as does not
introduce a big loss of information. In order to fuse these different maps, two
different fusion strategies known as the addition strategy and the l1-norm
strategy have been used. These strategies allowed to detect the whole object
while excluding the background. Extensive experiments show that HCLNet obtains
better performance than state-of-the-art methods.
