Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation

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 …

attention challenges computer computer vision cs.ai cs.cv global image information labels segmentation semantic spatial supervised learning supervision swin swin transformer through transformer vision