  The research on recognizing the most discriminative regions provides
referential information for weakly supervised object localization with only
image-level annotations. However, the most discriminative regions usually
conceal the other parts of the object, thereby impeding entire object
recognition and localization. To tackle this problem, the Dual-attention
Focused Module (DFM) is proposed to enhance object localization performance.
Specifically, we present a dual attention module for information fusion,
consisting of a position branch and a channel one. In each branch, the input
feature map is deduced into an enhancement map and a mask map, thereby
highlighting the most discriminative parts or hiding them. For the position
mask map, we introduce a focused matrix to enhance it, which utilizes the
principle that the pixels of an object are continuous. Between these two
branches, the enhancement map is integrated with the mask map, aiming at
partially compensating the lost information and diversifies the features. With
the dual-attention module and focused matrix, the entire object region could be
precisely recognized with implicit information. We demonstrate outperforming
results of DFM in experiments. In particular, DFM achieves state-of-the-art
performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.
