  Classification activation map (CAM), utilizing the classification structure
to generate pixel-wise localization maps, is a crucial mechanism for weakly
supervised object localization (WSOL). However, CAM directly uses the
classifier trained on image-level features to locate objects, making it prefers
to discern global discriminative factors rather than regional object cues. Thus
only the discriminative locations are activated when feeding pixel-level
features into this classifier. To solve this issue, this paper elaborates a
plug-and-play mechanism called BagCAMs to better project a well-trained
classifier for the localization task without refining or re-training the
baseline structure. Our BagCAMs adopts a proposed regional localizer generation
(RLG) strategy to define a set of regional localizers and then derive them from
a well-trained classifier. These regional localizers can be viewed as the base
learner that only discerns region-wise object factors for localization tasks,
and their results can be effectively weighted by our BagCAMs to form the final
localization map. Experiments indicate that adopting our proposed BagCAMs can
improve the performance of baseline WSOL methods to a great extent and obtains
state-of-the-art performance on three WSOL benchmarks. Code are released at
https://github.com/zh460045050/BagCAMs.
