  This work addresses the task of weakly-supervised object localization. The
goal is to learn object localization using only image-level class labels, which
are much easier to obtain compared to bounding box annotations. This task is
important because it reduces the need for labor-intensive ground-truth
annotations. However, methods for object localization trained using weak
supervision often suffer from limited accuracy in localization. To address this
challenge and enhance localization accuracy, we propose a multiscale object
localization transformer (MOLT). It comprises multiple object localization
transformers that extract patch embeddings across various scales. Moreover, we
introduce a deep clustering-guided refinement method that further enhances
localization accuracy by utilizing separately extracted image segments. These
segments are obtained by clustering pixels using convolutional neural networks.
Finally, we demonstrate the effectiveness of our proposed method by conducting
experiments on the publicly available ILSVRC-2012 dataset.
