Improving Weakly-Supervised Object Localization Using Adversarial
  Erasing and Pseudo Label
Byeongkeun Kang Sinhae Cha Yeejin Lee
Weakly-supervised learning approaches have gained significant attention due
to their ability to reduce the effort required for human annotations in
training neural networks. This paper investigates a framework for
weakly-supervised object localization, which aims to train a neural network
capable of predicting both the object class and its location using only images
and their image-level class labels. The proposed framework consists of a shared
feature extractor, a classifier, and a localizer. The localizer predicts
pixel-level class probabilities, while the classifier predicts the object class
at the image level. Since image-level class labels are insufficient for
training the localizer, weakly-supervised object localization methods often
encounter challenges in accurately localizing the entire object region. To
address this issue, the proposed method incorporates adversarial erasing and
pseudo labels to improve localization accuracy. Specifically, novel losses are
designed to utilize adversarially erased foreground features and adversarially
erased feature maps, reducing dependence on the most discriminative region.
Additionally, the proposed method employs pseudo labels to suppress activation
values in the background while increasing them in the foreground. The proposed
method is applied to two backbone networks (MobileNetV1 and InceptionV3) and is
evaluated on three publicly available datasets (ILSVRC-2012, CUB-200-2011, and
PASCAL VOC 2012). The experimental results demonstrate that the proposed method
outperforms previous state-of-the-art methods across all evaluated metrics.