In recent years, weakly supervised semantic segmentation using image-level
labels as supervision has received significant attention in the field of
computer vision. Most existing methods have addressed the challenges arising
from the lack of spatial information in these labels by focusing on
facilitating supervised learning through the generation of pseudo-labels from
class activation maps (CAMs). Due to the localized pattern detection of
Convolutional Neural Networks (CNNs), CAMs often emphasize only the most
discriminative parts of an object, making it challenging to accurately
distinguish foreground objects from each other and the background. Recent
studies have shown that Vision Transformer (ViT) features, due to their global
view, are more effective in capturing the scene layout than CNNs. However, the
use of hierarchical ViTs has not been extensively explored in this field. This
work explores the use of Swin Transformer by proposing "SWTformer" to enhance
the accuracy of the initial seed CAMs by bringing local and global views
together. SWTformer-V1 generates class probabilities and CAMs using only the
patch tokens as features. SWTformer-V2 incorporates a multi-scale feature
fusion mechanism to extract additional information and utilizes a
background-aware mechanism to generate more accurate localization maps with
improved cross-object discrimination. Based on experiments on the PascalVOC
2012 dataset, SWTformer-V1 achieves a 0.98% mAP higher localization accuracy,
outperforming state-of-the-art models. It also yields comparable performance by
0.82% mIoU on average higher than other methods in generating initial
localization maps, depending only on the classification network. SWTformer-V2
further improves the accuracy of the generated seed CAMs by 5.32% mIoU, further
proving the effectiveness of the local-to-global view provided by the Swin
transformer.

近年来，计算机视觉领域中的弱监督语义分割使用基于图像级标签的监督引起了广泛关注。本研究探索了使用 Swin Transformer 的 'SWTformer' 来提高初始种子 CAMs 的准确性，通过结合局部和全局视角。