Weakly supervised instance segmentation via class double-activation maps and boundary localization
J Peng, Y Wang, Z Pan - Signal Processing: Image Communication, 2024 - Elsevier
Weakly supervised instance segmentation based on image-level class labels has recently
gained much attention, in which the primary key step is to generate the pseudo labels based
on class activation maps (CAMs). Most methods adopt binary cross-entropy (BCE) loss to
train the classification model. However, since BCE loss is not class mutually exclusive,
activations among classes occur independently. Thus, not only do foreground classes are
wrongly activated as background, but also incorrect activations among confusing classes …
gained much attention, in which the primary key step is to generate the pseudo labels based
on class activation maps (CAMs). Most methods adopt binary cross-entropy (BCE) loss to
train the classification model. However, since BCE loss is not class mutually exclusive,
activations among classes occur independently. Thus, not only do foreground classes are
wrongly activated as background, but also incorrect activations among confusing classes …