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 https://github.com/arvijj/hfpl.

翻译：近年来，基于图像级标签的弱监督语义分割（WSSS）任务备受关注，因其显著降低了训练分割模型所需的大量数据标注成本。典型WSSS方法通过训练图像分类网络，在卷积特征图上应用全局平均池化（GAP），从而基于类激活图（CAM）估计目标位置，并识别图像区域的重要性。随后，CAM被用于生成以分割掩码形式存在的伪标签，在缺少像素级真实标签的情况下监督分割模型。以SEAM基线为例，先前工作通过两种方式改进CAM学习：（1）重要性采样——作为GAP的替代方案；（2）特征相似性损失——利用目标轮廓几乎完全与图像色彩边缘对齐的启发式规则。本研究提出对这些技术中CAM的概率解释进行改进，使似然函数比多项后验分布更具适用性。据此，我们提出一种可增强几乎所有现有WSSS方法的附加技术，能够同时提升各实现基线模型的区域相似性与轮廓质量。在PASCAL VOC数据集上的多个基线模型验证了该方法的有效性，而MS COCO数据集的实验表明，该方法在大规模场景下同样能获得性能增益。我们的代码已开源至https://github.com/arvijj/hfpl。