In weakly-supervised semantic segmentation (WSSS) using only image-level class labels, a problem with CNN-based Class Activation Maps (CAM) is that they tend to activate the most discriminative local regions of objects. On the other hand, methods based on Transformers learn global features but suffer from the issue of background noise contamination. This paper focuses on addressing the issue of background noise in attention weights within the existing WSSS method based on Conformer, known as TransCAM. The proposed method successfully reduces background noise, leading to improved accuracy of pseudo labels. Experimental results demonstrate that our model achieves segmentation performance of 70.5% on the PASCAL VOC 2012 validation data, 71.1% on the test data, and 45.9% on MS COCO 2014 data, outperforming TransCAM in terms of segmentation performance.

基于Conformer的TransCAM方法在弱监督语义分割中解决了背景噪声的问题，减少了背景噪声的影响，提高了伪标签的准确性。在实验中，我们的模型在PASCAL VOC 2012验证数据上达到了70.5%的分割性能，在测试数据上达到了71.1%的分割性能，在MS COCO 2014数据上达到了45.9%的分割性能，优于TransCAM方法。

弱监督语义分割的注意力图背景噪声消除