  Weakly supervised object localization is a challenging task which aims to
localize objects with coarse annotations such as image categories. Existing
deep network approaches are mainly based on class activation map, which focuses
on highlighting discriminative local region while ignoring the full object. In
addition, the emerging transformer-based techniques constantly put a lot of
emphasis on the backdrop that impedes the ability to identify complete objects.
To address these issues, we present a re-attention mechanism termed token
refinement transformer (TRT) that captures the object-level semantics to guide
the localization well. Specifically, TRT introduces a novel module named token
priority scoring module (TPSM) to suppress the effects of background noise
while focusing on the target object. Then, we incorporate the class activation
map as the semantically aware input to restrain the attention map to the target
object. Extensive experiments on two benchmarks showcase the superiority of our
proposed method against existing methods with image category annotations.
Source code is available in
\url{https://github.com/su-hui-zz/ReAttentionTransformer}.
