论文标题
ISIM：迭代性自我改进的模型，用于弱监督分段
ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation
论文作者
论文摘要
弱监督的语义细分（WSSS）是一项具有挑战性的任务，旨在从班级标签中学习细分标签。在文献中，利用从类激活图（CAM）获得的信息广泛用于WSSS研究。但是，由于CAM是从分类网络获得的，因此它们对对象的最歧视部分感兴趣，从而为分割任务产生了不完整的先验信息。在这项研究中，为了获得与分割标签的更连贯的凸轮，我们提出了一个框架，该框架在基于修改的编码器基于修饰的分割模型中采用迭代方法，同时支持分类和分割任务。由于没有给出地面真相分割标签，因此相同的模型还借助密集的条件随机场（DCRF）生成伪分段标签。结果，提出的框架成为了迭代的自我改进模型。使用DEEPLABV3和UNET模型执行的实验在Pascal VOC12数据集上显示出显着的增长，而DeepLabV3应用程序将当前的最新指标提高了％2.5。可以找到与实验相关的实现：https：//github.com/cenkbircanoglu/isim。
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.
