Extracting class activation maps (CAM) from a classification model often
results in poor coverage on foreground objects, i.e., only the discriminative
region (e.g., the "head" of "sheep") is recognized and the rest (e.g., the
"leg" of "sheep") mistakenly as background. The crux behind is that the weight
of the classifier (used to compute CAM) captures only the discriminative
features of objects. We tackle this by introducing a new computation method for
CAM that explicitly captures non-discriminative features as well, thereby
expanding CAM to cover whole objects. Specifically, we omit the last pooling
layer of the classification model, and perform clustering on all local features
of an object class, where "local" means "at a spatial pixel position". We call
the resultant K cluster centers local prototypes - represent local semantics
like the "head", "leg", and "body" of "sheep". Given a new image of the class,
we compare its unpooled features to every prototype, derive K similarity
matrices, and then aggregate them into a heatmap (i.e., our CAM). Our CAM thus
captures all local features of the class without discrimination. We evaluate it
in the challenging tasks of weakly-supervised semantic segmentation (WSSS), and
plug it in multiple state-of-the-art WSSS methods, such as MCTformer and AMN,
by simply replacing their original CAM with ours. Our extensive experiments on
standard WSSS benchmarks (PASCAL VOC and MS COCO) show the superiority of our
method: consistent improvements with little computational overhead.

提出了一种新的计算 CAM 的方法，通过引入一种新的计算方法，可以显式地捕捉到不具有识别能力的对象特征，从而将 CAM 扩展到覆盖整个对象，实验结果表明，该方法在弱监督语义分割领域具有优越性，且计算成本较低。