Weakly Supervised Semantic Segmentation (WSSS) employs weak supervision, such
as image-level labels, to train the segmentation model. Despite the impressive
achievement in recent WSSS methods, we identify that introducing weak labels
with high mean Intersection of Union (mIoU) does not guarantee high
segmentation performance. Existing studies have emphasized the importance of
prioritizing precision and reducing noise to improve overall performance. In
the same vein, we propose ORANDNet, an advanced ensemble approach tailored for
WSSS. ORANDNet combines Class Activation Maps (CAMs) from two different
classifiers to increase the precision of pseudo-masks (PMs). To further
mitigate small noise in the PMs, we incorporate curriculum learning. This
involves training the segmentation model initially with pairs of smaller-sized
images and corresponding PMs, gradually transitioning to the original-sized
pairs. By combining the original CAMs of ResNet-50 and ViT, we significantly
improve the segmentation performance over the single-best model and the naive
ensemble model, respectively. We further extend our ensemble method to CAMs
from AMN (ResNet-like) and MCTformer (ViT-like) models, achieving performance
benefits in advanced WSSS models. It highlights the potential of our ORANDNet
as a final add-on module for WSSS models.

ORANDNet 是一种针对弱监督语义分割的新型集成方法，利用两个不同分类器的类激活图和课程学习，提高了伪掩模的精确性并减轻了噪声，从而显著提高了分割性能。基于此方法的扩展同样适用于其他高级弱监督语义分割模型，表明 ORANDNet 具有作为弱监督语义分割模型的最终插件的潜力。