  Extracting class activation maps (CAM) is arguably the most standard step of
generating pseudo masks for weakly-supervised semantic segmentation (WSSS).
Yet, we find that the crux of the unsatisfactory pseudo masks is the binary
cross-entropy loss (BCE) widely used in CAM. Specifically, due to the
sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to
multiple classes co-occurring in the same receptive field. As a result, given a
class, its hot CAM pixels may wrongly invade the area belonging to other
classes, or the non-hot ones may be actually a part of the class. To this end,
we introduce an embarrassingly simple yet surprisingly effective method:
Reactivating the converged CAM with BCE by using softmax cross-entropy loss
(SCE), dubbed \textbf{ReCAM}. Given an image, we use CAM to extract the feature
pixels of each single class, and use them with the class label to learn another
fully-connected layer (after the backbone) with SCE. Once converged, we extract
ReCAM in the same way as in CAM. Thanks to the contrastive nature of SCE, the
pixel response is disentangled into different classes and hence less mask
ambiguity is expected. The evaluation on both PASCAL VOC and MS~COCO shows that
ReCAM not only generates high-quality masks, but also supports plug-and-play in
any CAM variant with little overhead.
