  Existing studies in weakly supervised semantic segmentation (WSSS) have
utilized class activation maps (CAMs) to localize the class objects. However,
since a classification loss is insufficient for providing precise object
regions, CAMs tend to be biased towards discriminative patterns (i.e.,
sparseness) and do not provide precise object boundary information (i.e.,
impreciseness). To resolve these limitations, we propose a novel framework
(composed of MainNet and SupportNet.) that derives pixel-level self-supervision
from given image-level supervision. In our framework, with the help of the
proposed Regional Contrastive Module (RCM) and Multi-scale Attentive Module
(MAM), MainNet is trained by self-supervision from the SupportNet. The RCM
extracts two forms of self-supervision from SupportNet: (1) class region masks
generated from the CAMs and (2) class-wise prototypes obtained from the
features according to the class region masks. Then, every pixel-wise feature of
the MainNet is trained by the prototype in a contrastive manner, sharpening the
resulting CAMs. The MAM utilizes CAMs inferred at multiple scales from the
SupportNet as self-supervision to guide the MainNet. Based on the dissimilarity
between the multi-scale CAMs from MainNet and SupportNet, CAMs from the MainNet
are trained to expand to the less-discriminative regions. The proposed method
shows state-of-the-art WSSS performance both on the train and validation sets
on the PASCAL VOC 2012 dataset. For reproducibility, code will be available
publicly soon.
