  Seed area generation is usually the starting point of weakly supervised
semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a
multi-label classification network is the de facto paradigm for seed area
generation, but CAMs generated from Convolutional Neural Networks (CNNs) and
Transformers are prone to be under- and over-activated, respectively, which
makes the strategies to refine CAMs for CNNs usually inappropriate for
Transformers, and vice versa. In this paper, we propose a Unified optimization
paradigm for Seed Area GEneration (USAGE) for both types of networks, in which
the objective function to be optimized consists of two terms: One is a
generation loss, which controls the shape of seed areas by a temperature
parameter following a deterministic principle for different types of networks;
The other is a regularization loss, which ensures the consistency between the
seed areas that are generated by self-adaptive network adjustment from
different views, to overturn false activation in seed areas. Experimental
results show that USAGE consistently improves seed area generation for both
CNNs and Transformers by large margins, e.g., outperforming state-of-the-art
methods by a mIoU of 4.1% on PASCAL VOC. Moreover, based on the USAGE-generated
seed areas on Transformers, we achieve state-of-the-art WSSS results on both
PASCAL VOC and MS COCO.
