  Although recent advances in deep learning accelerated an improvement in a
weakly supervised object localization (WSOL) task, there are still challenges
to identify the entire body of an object, rather than only discriminative
parts. In this paper, we propose a novel residual fine-grained attention (RFGA)
module that autonomously excites the less activated regions of an object by
utilizing information distributed over channels and locations within feature
maps in combination with a residual operation. To be specific, we devise a
series of mechanisms of triple-view attention representation, attention
expansion, and feature calibration. Unlike other attention-based WSOL methods
that learn a coarse attention map, having the same values across elements in
feature maps, our proposed RFGA learns fine-grained values in an attention map
by assigning different attention values for each of the elements. We validated
the superiority of our proposed RFGA module by comparing it with the recent
methods in the literature over three datasets. Further, we analyzed the effect
of each mechanism in our RFGA and visualized attention maps to get insights.
