  Self-supervised vision transformers can generate accurate localization maps
of the objects in an image. However, since they decompose the scene into
multiple maps containing various objects, and they do not rely on any explicit
supervisory signal, they cannot distinguish between the object of interest from
other objects, as required in weakly-supervised object localization (WSOL). To
address this issue, we propose leveraging the multiple maps generated by the
different transformer heads to acquire pseudo-labels for training a WSOL model.
In particular, a new discriminative proposals sampling method is introduced
that relies on a pretrained 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 CUB benchmark dataset indicate that our proposed approach can
outperform state-of-art methods over a wide range of threshold values. Our
method provides class activation maps with a better coverage of foreground
object regions w.r.t. the background.
