  Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the
gap for semantic segmentation performance from pixel-level supervision to
image-level supervision. Most advanced approaches are based on class activation
maps (CAMs) to generate pseudo-labels to train the segmentation network. The
main limitation of WSSS is that the process of generating pseudo-labels from
CAMs that use an image classifier is mainly focused on the most discriminative
parts of the objects. To address this issue, we propose Puzzle-CAM, a process
that minimizes differences between the features from separate patches and the
whole image. Our method consists of a puzzle module and two regularization
terms to discover the most integrated region in an object. Puzzle-CAM can
activate the overall region of an object using image-level supervision without
requiring extra parameters. % In experiments, Puzzle-CAM outperformed previous
state-of-the-art methods using the same labels for supervision on the PASCAL
VOC 2012 test dataset. In experiments, Puzzle-CAM outperformed previous
state-of-the-art methods using the same labels for supervision on the PASCAL
VOC 2012 dataset. Code associated with our experiments is available at
https://github.com/OFRIN/PuzzleCAM.
