Weakly Supervised Semantic Segmentation (WSSS) is a challenging task aiming to learn the segmentation labels from class-level labels. In the literature, exploiting the information obtained from Class Activation Maps (CAMs) is widely used for WSSS studies. However, as CAMs are obtained from a classification network, they are interested in the most discriminative parts of the objects, producing non-complete prior information for segmentation tasks. In this study, to obtain more coherent CAMs with segmentation labels, we propose a framework that employs an iterative approach in a modified encoder-decoder-based segmentation model, which simultaneously supports classification and segmentation tasks. As no ground-truth segmentation labels are given, the same model also generates the pseudo-segmentation labels with the help of dense Conditional Random Fields (dCRF). As a result, the proposed framework becomes an iterative self-improved model. The experiments performed with DeepLabv3 and UNet models show a significant gain on the Pascal VOC12 dataset, and the DeepLabv3 application increases the current state-of-the-art metric by \%2.5. The implementation associated with the experiments can be found: https://github.com/cenkbircanoglu/isim.
翻译:在文献中,利用分类活化图(CAMs)获得的信息被广泛用于SWSS的研究。然而,由于CAM是从分类网络中获得的,因此它们感兴趣的是对象中最具歧视性的部分,为分化任务提供不完全的先前信息。在这项研究中,为了获得更一致的分类标签,我们提议了一个框架,在修改的编码器-脱coder-基于分类的分解模型中采用迭代方法,同时支持分类和分解任务。由于没有提供地面分解标签,同样的模型还产生假分解标签,同时借助密集的调控随机场(dCRF)的帮助。因此,拟议的框架变成了一个迭代性自我简化模型。与DeepLabv3和UNet模型一起进行的实验显示,Pascal VOC12-decoder-bder-基础的分解模型取得了显著的收益,同时支持分类和分解任务。由于没有提供地面分解图分解标签,因此,同样的模型还生成了假分解标签标签。 与DeepLv3号测试增加了当前数据库/Mexqual-comsiabsical的模型。