语义分割,在机器学习上,多指对一段文本或者一张图片,提取其中有意义的部分,我们将这些有意义的部分称为语义单元,将这些语义单元提取出来的过程,称为语义分割。

VIP内容

论文题目

不同图像域弱监督语义分割的综合分析,A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains

论文摘要

最近提出的弱监督语义分割方法,虽然只训练了缺乏位置信息的图像标签,但在预测像素类方面取得了显著的效果。由于image注释的生成成本低、速度快,weaksupervision更适合于训练特定数据集中的分割算法。这些方法在自然场景图像上的应用还很不成熟,是否可以简单地移植到组织病理学、卫星图像等具有不同特征的领域,仍然有很好的应用前景。在将弱监督方法应用于这些其他图像域方面的研究文献很少;如何确定某些方法是否更适合于强制确定数据集,以及如何确定用于新数据集的最佳方法是未知的。本文评估了在自然场景、组织病理学和卫星图像数据集上的弱监督语义分割方法的现状。我们还分析了各种方法对每个数据集的兼容性,并提出了在不可见的图像数据集上应用弱监督语义分割的一些原则。

论文作者

Lyndon Chan ,Mahdi S. Hosseini,Konstantinos N. Plataniotis

成为VIP会员查看完整内容
7+
0+
更多VIP内容

最新论文

Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collection and annotation of such datasets comes at significant efforts and associated costs. In addition, visual data might contain private or sensitive information, that makes it equally unsuited for public release. Unfortunately, recent work on membership inference in the broader area of adversarial machine learning and inference attacks on machine learning models has shown that even black box classifiers leak information on the dataset that they were trained on. We present the first attacks and defenses for complex, state of the art models for semantic segmentation. In order to mitigate the associated risks, we also study a series of defenses against such membership inference attacks and find effective counter measures against the existing risks. Finally, we extensively evaluate our attacks and defenses on a range of relevant real-world datasets: Cityscapes, BDD100K, and Mapillary Vistas.

0+
0+
下载
预览
更多最新论文
Top