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
this https URL

提出了一种新的方法，Puzzle-CAM，用于在使用图像级监督的情况下，通过最小化从分离补丁和整个图像的特征之间的差异来发现对象中最集成的区域，以激活对象的整体区域。在 PASCAL VOC 2012 测试数据集上，Puzzle-CAM 的表现优于以前的最先进的方法。