The task of image-level weakly-supervised semantic segmentation (WSSS) has
gained popularity in recent years, as it reduces the vast data annotation cost
for training segmentation models. The typical approach for WSSS involves
training an image classification network using global average pooling (GAP) on
convolutional feature maps. This enables the estimation of object locations
based on class activation maps (CAMs), which identify the importance of image
regions. The CAMs are then used to generate pseudo-labels, in the form of
segmentation masks, to supervise a segmentation model in the absence of
pixel-level ground truth. In case of the SEAM baseline, a previous work
proposed to improve CAM learning in two ways: (1) Importance sampling, which is
a substitute for GAP, and (2) the feature similarity loss, which utilizes a
heuristic that object contours almost exclusively align with color edges in
images. In this work, we propose a different probabilistic interpretation of
CAMs for these techniques, rendering the likelihood more appropriate than the
multinomial posterior. As a result, we propose an add-on method that can boost
essentially any previous WSSS method, improving both the region similarity and
contour quality of all implemented state-of-the-art baselines. This is
demonstrated on a wide variety of baselines on the PASCAL VOC dataset.
Experiments on the MS COCO dataset show that performance gains can also be
achieved in a large-scale setting. Our code is available at
this https URL

该论文提出了一种基于图片分类网络和概率解释 CAM 的图像级弱监督语义分割方法来改善先前弱监督下模型的性能，通过提高区域相似性和轮廓质量来增强几乎任何以前的 WSSS 方法，并在 PASCAL VOC 数据集上进行了演示。