Hierarchical Complementary Learning for Weakly Supervised Object Localization

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 [1, 2, 3], or the image [4]. 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 hyperparameters 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 hyperparameters to generate different CAMs, as well as does not introduce a big loss ?Fully documented templates are available in the elsarticle package on CTAN. ∗Corresponding author Email addresses: benassou.narimene@hit.edu.cn (Sabrina Narimene Benassou), wzhshi@szu.edu.cn (Wuzhen Shi), fjiang@hit.edu.cn (Feng Jiang), Abdallah.benzine@digeiz.com (Abdallah Benzine) Preprint submitted to Journal of LTEX Templates November 17, 2020 ar X iv :2 01 1. 08 01 4v 1 [ cs .C V ] 1 6 N ov 2 02 0 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.