Puzzle-CAM: Improved localization via matching partial and full features

Understand

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.