  Drones are employed in a growing number of visual recognition applications. A
recent development in cell tower inspection is drone-based asset surveillance,
where the autonomous flight of a drone is guided by localizing objects of
interest in successive aerial images. In this paper, we propose a method to
train deep weakly-supervised object localization (WSOL) models based only on
image-class labels to locate object with high confidence. To train our
localizer, pseudo labels are efficiently harvested from a self-supervised
vision transformers (SSTs). However, since SSTs decompose the scene into
multiple maps containing various object parts, and do not rely on any explicit
supervisory signal, they cannot distinguish between the object of interest and
other objects, as required WSOL. To address this issue, we propose leveraging
the multiple maps generated by the different transformer heads to acquire
pseudo-labels for training a deep WSOL model. In particular, a new
Discriminative Proposals Sampling (DiPS) method is introduced that relies on a
CNN classifier to identify discriminative regions. Then, foreground and
background pixels are sampled from these regions in order to train a WSOL model
for generating activation maps that can accurately localize objects belonging
to a specific class. Empirical results on the challenging TelDrone dataset
indicate that our proposed approach can outperform state-of-art methods over a
wide range of threshold values over produced maps. We also computed results on
CUB dataset, showing that our method can be adapted for other tasks.
